1000008600010@unknown@formal@none@1@S@⌊δText corpusδ⌋@@@@1@2@@oe@26-8-2013
1000008600020@unknown@formal@none@1@S@In ⌊>linguistics>⌋, a ⌊∗corpus∗⌋ (plural ⌊/corpora/⌋) or ⌊∗text corpus∗⌋ is a large and structured set of texts (now usually electronically stored and processed).@@@@1@23@@oe@26-8-2013
1000008600030@unknown@formal@none@1@S@They are used to do statistical analysis, checking occurrences or validating linguistic rules on a specific universe.@@@@1@17@@oe@26-8-2013
1000008600040@unknown@formal@none@1@S@A corpus may contain texts in a single language (⌊/monolingual corpus/⌋) or text data in multiple languages (⌊/multilingual corpus/⌋).@@@@1@19@@oe@26-8-2013
1000008600050@unknown@formal@none@1@S@Multilingual corpora that have been specially formatted for side-by-side comparison are called ⌊/aligned parallel corpora/⌋.@@@@1@15@@oe@26-8-2013
1000008600060@unknown@formal@none@1@S@In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as ⌊>annotation>⌋.@@@@1@22@@oe@26-8-2013
1000008600070@unknown@formal@none@1@S@An example of annotating a corpus is ⌊>part-of-speech tagging>⌋, or ⌊/POS-tagging/⌋, in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of ⌊/tags/⌋.@@@@1@34@@oe@26-8-2013
1000008600080@unknown@formal@none@1@S@Another example is indicating the ⌊>lemma>⌋ (base) form of each word.@@@@1@11@@oe@26-8-2013
1000008600090@unknown@formal@none@1@S@When the language of the corpus is not a working language of the researchers who use it, interlinear ⌊>gloss>⌋ing is used to make the annotation bilingual.@@@@1@26@@oe@26-8-2013
1000008600100@unknown@formal@none@1@S@Corpora are the main knowledge base in ⌊>corpus linguistics>⌋.@@@@1@9@@oe@26-8-2013
1000008600110@unknown@formal@none@1@S@The analysis and processing of various types of corpora are also the subject of much work in ⌊>computational linguistics>⌋, ⌊>speech recognition>⌋ and ⌊>machine translation>⌋, where they are often used to create ⌊>hidden Markov model>⌋s for POS-tagging and other purposes.@@@@1@39@@oe@26-8-2013
1000008600120@unknown@formal@none@1@S@Corpora and ⌊>frequency list>⌋s derived from them are useful for ⌊>language teaching>⌋.@@@@1@12@@oe@26-8-2013
1000008600130@unknown@formal@none@1@S@⌊=Archaeological corpora¦2=⌋@@@@1@2@@oe@26-8-2013
1000008600140@unknown@formal@none@1@S@Text corpora. are also used in the study of ⌊>historical document>⌋s, for example in attempts to ⌊>decipher>⌋ ancient scripts, or in ⌊>Biblical scholarship>⌋.@@@@1@23@@oe@26-8-2013
1000008600150@unknown@formal@none@1@S@Some archaeological corpora can be of such short duration that they provide a snapshot in time.@@@@1@16@@oe@26-8-2013
1000008600160@unknown@formal@none@1@S@One of the shortest corpora in time, may be the 15-30 year ⌊>Amarna letters>⌋ texts-(⌊>1350 BC>⌋).@@@@1@16@@oe@26-8-2013
1000008600170@unknown@formal@none@1@S@The ⌊/corpus/⌋ of an ancient city, (for example the "⌊>Kültepe>⌋ Texts" of Turkey), may go through a series of corpora, determined by their find site dates.@@@@1@26@@oe@26-8-2013
1000008700010@unknown@formal@none@1@S@⌊δText miningδ⌋@@@@1@2@@oe@26-8-2013
1000008700020@unknown@formal@none@1@S@⌊∗Text mining∗⌋, sometimes alternately referred to as ⌊/text ⌊>data mining>⌋/⌋, refers generally to the process of deriving high quality ⌊>information>⌋ from text.@@@@1@22@@oe@26-8-2013
1000008700030@unknown@formal@none@1@S@High quality information is typically derived through the dividing of patterns and trends through means such as ⌊>statistical pattern learning>⌋.@@@@1@20@@oe@26-8-2013
1000008700040@unknown@formal@none@1@S@Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a ⌊>database>⌋), deriving patterns within the structured data, and finally evaluation and interpretation of the output.@@@@1@47@@oe@26-8-2013
1000008700050@unknown@formal@none@1@S@'High quality' in text mining usually refers to some combination of ⌊>relevance>⌋, ⌊>novelty>⌋, and interestingness.@@@@1@15@@oe@26-8-2013
1000008700060@unknown@formal@none@1@S@Typical text mining tasks include ⌊>text categorization>⌋, ⌊>text clustering>⌋, ⌊>concept/entity extraction>⌋, production of granular taxonomies, ⌊>sentiment analysis>⌋, ⌊>document summarization>⌋, and entity relation modeling (⌊/i.e./⌋, learning relations between ⌊>named entities>⌋).@@@@1@29@@oe@26-8-2013
1000008700070@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000008700080@unknown@formal@none@1@S@Labour-intensive manual text-mining approaches first surfaced in the mid-1980s, but technological advances have enabled the field to advance swiftly during the past decade.@@@@1@23@@oe@26-8-2013
1000008700090@unknown@formal@none@1@S@Text mining is an ⌊>interdisciplinary>⌋ field which draws on ⌊>information retrieval>⌋, ⌊>data mining>⌋, ⌊>machine learning>⌋, ⌊>statistics>⌋, and ⌊>computational linguistics>⌋.@@@@1@19@@oe@26-8-2013
1000008700100@unknown@formal@none@1@S@As most information (over 80%) is currently stored as text, text mining is believed to have a high commercial potential value.@@@@1@21@@oe@26-8-2013
1000008700110@unknown@formal@none@1@S@Increasing interest is being paid to multilingual data mining: the ability to gain information across languages and cluster similar items from different linguistic sources according to their meaning.@@@@1@28@@oe@26-8-2013
1000008700120@unknown@formal@none@1@S@⌊=Sentiment analysis¦2=⌋@@@@1@2@@oe@26-8-2013
1000008700130@unknown@formal@none@1@S@⌊>Sentiment analysis>⌋ may, for example, involve analysis of movie reviews for estimating how favorably a review is for a movie.@@@@1@20@@oe@26-8-2013
1000008700140@unknown@formal@none@1@S@Such an analysis may require a labeled data set or labeling of the ⌊>affectivity>⌋ of words.@@@@1@16@@oe@26-8-2013
1000008700150@unknown@formal@none@1@S@A resource for affectivity of words has been made for ⌊>WordNet>⌋.@@@@1@11@@oe@26-8-2013
1000008700160@unknown@formal@none@1@S@⌊=Applications¦2=⌋@@@@1@1@@oe@26-8-2013
1000008700170@unknown@formal@none@1@S@Recently, text mining has been receiving attention in many areas.@@@@1@10@@oe@26-8-2013
1000008700180@unknown@formal@none@1@S@⌊=Security applications¦3=⌋@@@@1@2@@oe@26-8-2013
1000008700190@unknown@formal@none@1@S@One of the largest text mining applications that exists is probably the classified ⌊>ECHELON>⌋ surveillance system.@@@@1@16@@oe@26-8-2013
1000008700200@unknown@formal@none@1@S@Additionally, many text mining software packages such as ⌊>AeroText>⌋, ⌊>Attensity>⌋, ⌊>SPSS>⌋ and ⌊>Expert System>⌋ are marketed towards security applications, particularly analysis of plain text sources such as Internet news.@@@@1@29@@oe@26-8-2013
1000008700210@unknown@formal@none@1@S@In 2007, ⌊>Europol>⌋'s Serious Crime division developed an analysis system in order to track transnational organized crime.@@@@1@17@@oe@26-8-2013
1000008700220@unknown@formal@none@1@S@This Overall Analysis System for Intelligence Support (OASIS) integrates among the most advanced text analytics and text mining technologies available on today's market.@@@@1@23@@oe@26-8-2013
1000008700230@unknown@formal@none@1@S@This system led Europol to make the most significant progress to support law enforcement objectives at the international level.@@@@1@19@@oe@26-8-2013
1000008700240@unknown@formal@none@1@S@⌊=Biomedical applications¦3=⌋@@@@1@2@@oe@26-8-2013
1000008700250@unknown@formal@none@1@S@A range of applications of text mining of the biomedical literature has been described.@@@@1@14@@oe@26-8-2013
1000008700260@unknown@formal@none@1@S@One example is ⌊>PubGene>⌋ (⌊> pubgene.org>⌋) that combines biomedical text mining with network visualization as an Internet service.@@@@1@18@@oe@26-8-2013
1000008700270@unknown@formal@none@1@S@Another example, which uses ontologies with textmining is ⌊> GoPubMed.org>⌋.@@@@1@10@@oe@26-8-2013
1000008700280@unknown@formal@none@1@S@⌊=Software and applications¦3=⌋@@@@1@3@@oe@26-8-2013
1000008700290@unknown@formal@none@1@S@Research and development departments of major companies, including ⌊>IBM>⌋ and ⌊>Microsoft>⌋, are researching text mining techniques and developing programs to further automate the mining and analysis processes.@@@@1@27@@oe@26-8-2013
1000008700300@unknown@formal@none@1@S@Text mining software is also being researched by different companies working in the area of search and indexing in general as a way to improve their results.@@@@1@27@@oe@26-8-2013
1000008700310@unknown@formal@none@1@S@⌊=Marketing applications¦3=⌋@@@@1@2@@oe@26-8-2013
1000008700320@unknown@formal@none@1@S@Text mining is starting to be used in marketing as well, more specifically in analytical ⌊>Customer relationship management>⌋.@@@@1@18@@oe@26-8-2013
1000008700321@unknown@formal@none@1@S@⌊> Coussement and Van den Poel>⌋ (2008) apply it to improve ⌊>predictive analytics>⌋ models for customer churn (⌊>Customer attrition>⌋). .@@@@1@20@@oe@26-8-2013
1000008700330@unknown@formal@none@1@S@⌊=Academic applications¦3=⌋@@@@1@2@@oe@26-8-2013
1000008700340@unknown@formal@none@1@S@The issue of text mining is of importance to publishers who hold large ⌊>databases>⌋ of information requiring ⌊>indexing>⌋ for retrieval.@@@@1@20@@oe@26-8-2013
1000008700350@unknown@formal@none@1@S@This is particularly true in scientific disciplines, in which highly specific information is often contained within written text.@@@@1@18@@oe@26-8-2013
1000008700360@unknown@formal@none@1@S@Therefore, initiatives have been taken such as ⌊>Nature's>⌋ proposal for an Open Text Mining Interface (OTMI) and ⌊>NIH's>⌋ common Journal Publishing ⌊>Document Type Definition>⌋ (DTD) that would provide semantic cues to machines to answer specific queries contained within text without removing publisher barriers to public access.@@@@1@46@@oe@26-8-2013
1000008700370@unknown@formal@none@1@S@Academic institutions have also become involved in the text mining initiative:@@@@1@11@@oe@26-8-2013
1000008700380@unknown@formal@none@1@S@The ⌊>National Centre for Text Mining>⌋, a collaborative effort between the Universities of ⌊>Manchester>⌋ and ⌊>Liverpool>⌋, provides customised tools, research facilities and offers advice to the academic community.@@@@1@28@@oe@26-8-2013
1000008700390@unknown@formal@none@1@S@They are funded by the ⌊>Joint Information Systems Committee>⌋ (JISC) and two of the UK ⌊>Research Council>⌋s.@@@@1@17@@oe@26-8-2013
1000008700400@unknown@formal@none@1@S@With an initial focus on text mining in the ⌊>biological>⌋ and ⌊>biomedical>⌋ sciences, research has since expanded into the areas of ⌊>Social Science>⌋.@@@@1@23@@oe@26-8-2013
1000008700410@unknown@formal@none@1@S@In the United States, the ⌊>School of Information>⌋ at ⌊>University of California, Berkeley>⌋ is developing a program called BioText to assist bioscience researchers in text mining and analysis.@@@@1@28@@oe@26-8-2013
1000008700420@unknown@formal@none@1@S@⌊=Open-source software and applications¦3=⌋@@@@1@4@@oe@26-8-2013
1000008700430@unknown@formal@none@1@S@⌊•⌊#⌊>GATE>⌋ - natural language processing and language engineering tool.#⌋@@@@1@9@@oe@26-8-2013
1000008700440@unknown@formal@none@1@S@⌊#⌊>YALE/RapidMiner>⌋ with its Word Vector Tool plugin - data and text mining software.#⌋@@@@1@13@@oe@26-8-2013
1000008700450@unknown@formal@none@1@S@⌊#tm - text mining in the ⌊>R programming language>⌋#⌋•⌋@@@@1@9@@oe@26-8-2013
1000008700460@unknown@formal@none@1@S@⌊=Implications¦2=⌋@@@@1@1@@oe@26-8-2013
1000008700470@unknown@formal@none@1@S@Until recently websites most often used text-based lexical searches; in other words, users could find documents only by the words that happened to occur in the documents.@@@@1@27@@oe@26-8-2013
1000008700480@unknown@formal@none@1@S@Text mining may allow searches to be directly answered by the ⌊>semantic web>⌋; users may be able to search for content based on its meaning and context, rather than just by a specific word.@@@@1@34@@oe@26-8-2013
1000008700490@unknown@formal@none@1@S@Additionally, text mining software can be used to build large dossiers of information about specific people and events.@@@@1@18@@oe@26-8-2013
1000008700500@unknown@formal@none@1@S@For example, by using software that extracts specifics facts about businesses and individuals from news reports, large datasets can be built to facilitate ⌊>social networks analysis>⌋ or ⌊>counter-intelligence>⌋.@@@@1@28@@oe@26-8-2013
1000008700510@unknown@formal@none@1@S@In effect, the text mining software may act in a capacity similar to an ⌊>intelligence analyst>⌋ or ⌊>research librarian>⌋, albeit with a more limited scope of analysis.@@@@1@27@@oe@26-8-2013
1000008700520@unknown@formal@none@1@S@Text mining is also used in some email ⌊>spam filter>⌋s as a way of determining the characteristics of messages that are likely to be advertisements or other unwanted material.@@@@1@29@@oe@26-8-2013
1000008800010@unknown@formal@none@1@S@⌊δTranslationδ⌋@@@@1@1@@oe@26-8-2013
1000008800020@unknown@formal@none@1@S@⌊∗Translation∗⌋ is the action of ⌊>interpretation>⌋ of the ⌊>meaning>⌋ of a text, and subsequent production of an ⌊>equivalent>⌋ text, also called a ⌊∗translation∗⌋, that communicates the same ⌊>message>⌋ in another language.@@@@1@31@@oe@26-8-2013
1000008800030@unknown@formal@none@1@S@The text to be translated is called the ⌊>source text>⌋, and the language it is to be translated into is called the ⌊>target language>⌋; the final product is sometimes called the "target text."@@@@1@33@@oe@26-8-2013
1000008800040@unknown@formal@none@1@S@Translation must take into account constraints that include ⌊>context>⌋, the rules of ⌊>grammar>⌋ of the two languages, their writing ⌊>convention>⌋s, and their ⌊>idiom>⌋s.@@@@1@23@@oe@26-8-2013
1000008800050@unknown@formal@none@1@S@A common ⌊>misconception>⌋ is that there exists a simple ⌊>word-for-word>⌋ correspondence between any two ⌊>language>⌋s, and that translation is a straightforward ⌊>mechanical>⌋ process.@@@@1@23@@oe@26-8-2013
1000008800060@unknown@formal@none@1@S@A word-for-word translation does not take into account context, grammar, conventions, and idioms.@@@@1@13@@oe@26-8-2013
1000008800070@unknown@formal@none@1@S@Translation is fraught with the potential for "⌊>spilling over>⌋" of ⌊>idiom>⌋s and ⌊>usage>⌋s from one language into the other, since both languages repose within the single brain of the translator.@@@@1@30@@oe@26-8-2013
1000008800080@unknown@formal@none@1@S@Such spilling-over easily produces ⌊>linguistic hybrids>⌋ such as "⌊>Franglais>⌋" (⌊>French>⌋-⌊>English>⌋), "⌊>Spanglish>⌋" (⌊>Spanish>⌋-⌊>English>⌋), "⌊>Poglish>⌋" (⌊>Polish>⌋-⌊>English>⌋) and "⌊>Portuñol>⌋" (⌊>Portuguese>⌋-⌊>Spanish>⌋).@@@@1@17@@oe@26-8-2013
1000008800090@unknown@formal@none@1@S@The art of translation is as old as written ⌊>literature>⌋.@@@@1@10@@oe@26-8-2013
1000008800100@unknown@formal@none@1@S@Parts of the ⌊>Sumer>⌋ian ⌊/⌊>Epic of Gilgamesh>⌋/⌋, among the oldest known literary works, have been found in translations into several ⌊>Asia>⌋tic languages of the second millennium BCE.@@@@1@27@@oe@26-8-2013
1000008800110@unknown@formal@none@1@S@The ⌊/Epic of Gilgamesh/⌋ may have been read, in their own languages, by early authors of the ⌊/⌊>Bible>⌋/⌋ and of the ⌊/⌊>Iliad>⌋/⌋.@@@@1@22@@oe@26-8-2013
1000008800120@unknown@formal@none@1@S@With the advent of computers, attempts have been made to ⌊>computer>⌋ize or otherwise ⌊>automate>⌋ the translation of ⌊>natural-language>⌋ texts (⌊>machine translation>⌋) or to use computers as an ⌊/aid/⌋ to translation (⌊>computer-assisted translation>⌋).@@@@1@32@@oe@26-8-2013
1000008800130@unknown@formal@none@1@S@⌊=The term¦2=⌋@@@@1@2@@oe@26-8-2013
1000008800140@unknown@formal@none@1@S@⌊>Etymologically>⌋, "translation" is a "carrying across" or "bringing across."@@@@1@9@@oe@26-8-2013
1000008800150@unknown@formal@none@1@S@The ⌊>Latin>⌋ "⌊/translatio/⌋" derives from the ⌊>perfect>⌋ ⌊>passive>⌋ ⌊>participle>⌋, "⌊/translatum/⌋," of "⌊/transferre/⌋" ("to transfer" — from "⌊/trans/⌋," "across" + "⌊/ferre/⌋," "to carry" or "to bring").@@@@1@25@@oe@26-8-2013
1000008800160@unknown@formal@none@1@S@The modern ⌊>Romance>⌋, ⌊>Germanic>⌋ and ⌊>Slavic>⌋ ⌊>European languages>⌋ have generally formed their own ⌊>equivalent>⌋ terms for this concept after the Latin model — after "⌊/transferre/⌋" or after the kindred "⌊/traducere/⌋" ("to bring across" or "to lead across").@@@@1@37@@oe@26-8-2013
1000008800170@unknown@formal@none@1@S@Additionally, the ⌊>Greek>⌋ term for "translation," "⌊/metaphrasis/⌋" ("a speaking across"), has supplied ⌊>English>⌋ with "⌊>metaphrase>⌋" (a "⌊>literal translation>⌋," or "word-for-word" translation)—as contrasted with "⌊>paraphrase>⌋" ("a saying in other words," from the Greek "⌊/paraphrasis/⌋").@@@@1@33@@oe@26-8-2013
1000008800180@unknown@formal@none@1@S@"Metaphrase" equates, in one of the more recent terminologies, to "⌊>formal equivalence>⌋," and "paraphrase"—to "⌊>dynamic equivalence>⌋."@@@@1@16@@oe@26-8-2013
1000008800190@unknown@formal@none@1@S@⌊=Misconceptions¦2=⌋@@@@1@1@@oe@26-8-2013
1000008800200@unknown@formal@none@1@S@Newcomers to translation sometimes proceed as if translation were an ⌊>exact science>⌋ — as if consistent, one-to-one ⌊>correlation>⌋s existed between the words and phrases of different languages, rendering translations fixed and identically reproducible, much as in ⌊>cryptography>⌋.@@@@1@37@@oe@26-8-2013
1000008800210@unknown@formal@none@1@S@Such ⌊>novice>⌋s may assume that all that is needed to translate a text is to "⌊>encode>⌋" and "⌊>decode>⌋" equivalents between the two languages, using a ⌊>translation dictionary>⌋ as the "⌊>codebook>⌋."@@@@1@30@@oe@26-8-2013
1000008800220@unknown@formal@none@1@S@On the contrary, such a fixed relationship would only exist were a new language ⌊>synthesized>⌋ and simultaneously matched to a pre-existing language's scopes of ⌊>meaning>⌋, ⌊>etymologies>⌋, and ⌊>lexical>⌋ ⌊>ecological niche>⌋s.@@@@1@30@@oe@26-8-2013
1000008800230@unknown@formal@none@1@S@If the new language were subsequently to take on a life apart from such cryptographic use, each word would spontaneously begin to assume new shades of meaning and cast off previous ⌊>association>⌋s, thereby vitiating any such artificial synchronization.@@@@1@38@@oe@26-8-2013
1000008800240@unknown@formal@none@1@S@Henceforth translation would require the disciplines described in this article.@@@@1@10@@oe@26-8-2013
1000008800250@unknown@formal@none@1@S@Another common misconception is that ⌊/anyone/⌋ who can speak a ⌊>second language>⌋ will make a good translator.@@@@1@17@@oe@26-8-2013
1000008800260@unknown@formal@none@1@S@In the translation community, it is generally accepted that the best translations are produced by persons who are translating into their own ⌊>native language>⌋s, as it is rare for someone who has learned a second language to have total fluency in that language.@@@@1@43@@oe@26-8-2013
1000008800270@unknown@formal@none@1@S@A good translator understands the source language well, has specific experience in the subject matter of the text, and is a good writer in the target language.@@@@1@27@@oe@26-8-2013
1000008800280@unknown@formal@none@1@S@Moreover, he is not only ⌊>bilingual>⌋ but ⌊>bicultural>⌋.@@@@1@8@@oe@26-8-2013
1000008800290@unknown@formal@none@1@S@It has been debated whether translation is ⌊>art>⌋ or ⌊>craft>⌋.@@@@1@10@@oe@26-8-2013
1000008800300@unknown@formal@none@1@S@Literary translators, such as ⌊>Gregory Rabassa>⌋ in ⌊/If This Be Treason/⌋, argue that translation is an art—a teachable one.@@@@1@19@@oe@26-8-2013
1000008800310@unknown@formal@none@1@S@Other translators, mostly technical, commercial, and legal, regard their ⌊/métier/⌋ as a craft—again, a teachable one, subject to ⌊>linguistic analysis>⌋, that benefits from ⌊>academic>⌋ study.@@@@1@25@@oe@26-8-2013
1000008800320@unknown@formal@none@1@S@As with other human activities, the distinction between art and craft may be largely a matter of degree.@@@@1@18@@oe@26-8-2013
1000008800330@unknown@formal@none@1@S@Even a document which appears simple, e.g. a product ⌊>brochure>⌋, requires a certain level of linguistic skill that goes beyond mere technical terminology.@@@@1@23@@oe@26-8-2013
1000008800340@unknown@formal@none@1@S@Any material used for marketing purposes reflects on the company that produces the product and the brochure.@@@@1@17@@oe@26-8-2013
1000008800350@unknown@formal@none@1@S@The best translations are obtained through the combined application of good technical-terminology skills and good writing skills.@@@@1@17@@oe@26-8-2013
1000008800360@unknown@formal@none@1@S@Translation has served as a writing school for many recognized writers.@@@@1@11@@oe@26-8-2013
1000008800370@unknown@formal@none@1@S@Translators, including the early modern European translators of the ⌊/⌊>Bible>⌋/⌋, in the course of their work have shaped the very ⌊>language>⌋s into which they have translated.@@@@1@26@@oe@26-8-2013
1000008800380@unknown@formal@none@1@S@They have acted as bridges for conveying knowledge and ideas between ⌊>culture>⌋s and ⌊>civilization>⌋s.@@@@1@14@@oe@26-8-2013
1000008800390@unknown@formal@none@1@S@Along with ⌊>idea>⌋s, they have imported into their own languages, ⌊>calque>⌋s of ⌊>grammatical structures>⌋ and of ⌊>vocabulary>⌋ from the ⌊>source language>⌋s.@@@@1@21@@oe@26-8-2013
1000008800400@unknown@formal@none@1@S@⌊=Interpreting¦2=⌋@@@@1@1@@oe@26-8-2013
1000008800410@unknown@formal@none@1@S@Interpreting, or "interpretation," is the intellectual activity that consists of facilitating ⌊>oral>⌋ or ⌊>sign-language>⌋ ⌊>communication>⌋, either simultaneously or consecutively, between two or among three or more speakers who are not speaking, or signing, the same language.@@@@1@36@@oe@26-8-2013
1000008800420@unknown@formal@none@1@S@The words "interpreting" and "interpretation" both can be used to refer to this activity; the word "interpreting" is commonly used in the profession and in the translation-studies field to avoid confusion with other meanings of the word "⌊>interpretation>⌋."@@@@1@38@@oe@26-8-2013
1000008800430@unknown@formal@none@1@S@Not all languages employ, as ⌊>English>⌋ does, two separate words to denote the activities of ⌊/written/⌋ and live-communication (⌊/oral/⌋ or ⌊/sign-language/⌋) translators.@@@@1@22@@oe@26-8-2013
1000008800440@unknown@formal@none@1@S@⌊=Fidelity vs. transparency¦2=⌋@@@@1@3@@oe@26-8-2013
1000008800450@unknown@formal@none@1@S@⌊>Fidelity>⌋ (or "faithfulness") and ⌊>transparency>⌋ are two qualities that, for millennia, have been regarded as ideals to be striven for in translation, particularly ⌊>literary>⌋ translation.@@@@1@25@@oe@26-8-2013
1000008800460@unknown@formal@none@1@S@These two ideals are often at odds.@@@@1@7@@oe@26-8-2013
1000008800470@unknown@formal@none@1@S@Thus a 17th-century French critic coined the phrase, "⌊/les belles infidèles/⌋," to suggest that translations, like women, could be ⌊/either/⌋ faithful ⌊/or/⌋ beautiful, but not both at the same time.@@@@1@30@@oe@26-8-2013
1000008800480@unknown@formal@none@1@S@Fidelity pertains to the extent to which a translation accurately renders the meaning of the ⌊>source text>⌋, without adding to or subtracting from it, without intensifying or weakening any part of the meaning, and otherwise without distorting it.@@@@1@38@@oe@26-8-2013
1000008800490@unknown@formal@none@1@S@⌊>Transparency>⌋ pertains to the extent to which a translation appears to a native speaker of the target language to have originally been written in that language, and conforms to the language's grammatical, syntactic and idiomatic conventions.@@@@1@36@@oe@26-8-2013
1000008800500@unknown@formal@none@1@S@A translation that meets the first criterion is said to be a "faithful translation"; a translation that meets the second criterion, an "⌊>idiomatic>⌋ translation."@@@@1@24@@oe@26-8-2013
1000008800510@unknown@formal@none@1@S@The two qualities are ⌊/not necessarily/⌋ mutually exclusive.@@@@1@8@@oe@26-8-2013
1000008800520@unknown@formal@none@1@S@The criteria used to judge the faithfulness of a translation vary according to the subject, the precision of the original contents, the type, function and use of the text, its literary qualities, its social or historical context, and so forth.@@@@1@40@@oe@26-8-2013
1000008800530@unknown@formal@none@1@S@The criteria for judging the ⌊>transparency>⌋ of a translation would appear more straightforward: an unidiomatic translation "sounds wrong," and in the extreme case of ⌊>word-for-word translation>⌋s generated by many ⌊>machine-translation>⌋ systems, often results in patent nonsense with only a ⌊>humor>⌋ous value (see "⌊>round-trip translation>⌋").@@@@1@44@@oe@26-8-2013
1000008800540@unknown@formal@none@1@S@Nevertheless, in certain contexts a translator may consciously ⌊/strive/⌋ to produce a literal translation.@@@@1@14@@oe@26-8-2013
1000008800550@unknown@formal@none@1@S@⌊>Literary>⌋ translators and translators of ⌊>religious>⌋ or ⌊>historic>⌋ texts often adhere as closely as possible to the source text.@@@@1@19@@oe@26-8-2013
1000008800560@unknown@formal@none@1@S@In doing so, they often deliberately stretch the boundaries of the target language to produce an unidiomatic text.@@@@1@18@@oe@26-8-2013
1000008800570@unknown@formal@none@1@S@Similarly, a literary translator may wish to adopt words or expressions from the ⌊>source language>⌋ in order to provide "local color" in the translation.@@@@1@24@@oe@26-8-2013
1000008800580@unknown@formal@none@1@S@In recent decades, prominent advocates of such "non-transparent" translation have included the French scholar ⌊>Antoine Berman>⌋, who identified twelve deforming tendencies inherent in most prose translations, and the American theorist Lawrence Venuti, who has called upon translators to apply "foreignizing" translation strategies instead of domesticating ones.@@@@1@46@@oe@26-8-2013
1000008800590@unknown@formal@none@1@S@Many non-transparent-translation theories draw on concepts from ⌊>German Romanticism>⌋, the most obvious influence on latter-day theories of "foreignization" being the German theologian and philosopher ⌊>Friedrich Schleiermacher>⌋.@@@@1@26@@oe@26-8-2013
1000008800600@unknown@formal@none@1@S@In his seminal lecture "On the Different Methods of Translation" (1813) he distinguished between translation methods that move "the writer toward [the reader]," i.e., ⌊>transparency>⌋, and those that move the "reader toward [the author]," i.e., an extreme ⌊>fidelity>⌋ to the foreignness of the ⌊>source text>⌋.@@@@1@45@@oe@26-8-2013
1000008800610@unknown@formal@none@1@S@Schleiermacher clearly favored the latter approach.@@@@1@6@@oe@26-8-2013
1000008800620@unknown@formal@none@1@S@His preference was motivated, however, not so much by a desire to embrace the foreign, as by a nationalist desire to oppose France's cultural domination and to promote ⌊>German literature>⌋.@@@@1@30@@oe@26-8-2013
1000008800630@unknown@formal@none@1@S@For the most part, current Western practices in translation are dominated by the concepts of "fidelity" and "transparency."@@@@1@18@@oe@26-8-2013
1000008800640@unknown@formal@none@1@S@This has not always been the case.@@@@1@7@@oe@26-8-2013
1000008800650@unknown@formal@none@1@S@There have been periods, especially in pre-Classical Rome and in the 18th century, when many translators stepped beyond the bounds of translation proper into the realm of ⌊/adaptation/⌋.@@@@1@28@@oe@26-8-2013
1000008800660@unknown@formal@none@1@S@Adapted translation retains currency in some non-Western traditions.@@@@1@8@@oe@26-8-2013
1000008800670@unknown@formal@none@1@S@Thus the ⌊>India>⌋n epic, the ⌊/⌊>Ramayana>⌋/⌋, appears in many versions in the various ⌊>Indian languages>⌋, and the stories are different in each.@@@@1@22@@oe@26-8-2013
1000008800680@unknown@formal@none@1@S@If one considers the words used for translating into the Indian languages, whether those be ⌊>Aryan>⌋ or ⌊>Dravidian>⌋ languages, he is struck by the freedom that is granted to the translators.@@@@1@31@@oe@26-8-2013
1000008800690@unknown@formal@none@1@S@This may relate to a devotion to ⌊>prophetic>⌋ passages that strike a deep religious chord, or to a vocation to instruct ⌊>unbeliever>⌋s.@@@@1@22@@oe@26-8-2013
1000008800700@unknown@formal@none@1@S@Similar examples are to be found in ⌊>medieval Christian>⌋ literature, which adjusted the text to the customs and values of the audience.@@@@1@22@@oe@26-8-2013
1000008800710@unknown@formal@none@1@S@⌊=Equivalence¦2=⌋@@@@1@1@@oe@26-8-2013
1000008800720@unknown@formal@none@1@S@The question of ⌊>fidelity>⌋ vs. ⌊>transparency>⌋ has also been formulated in terms of, respectively, "⌊/formal/⌋ equivalence" and "⌊/dynamic/⌋ equivalence."@@@@1@19@@oe@26-8-2013
1000008800730@unknown@formal@none@1@S@The latter two expressions are associated with the translator ⌊>Eugene Nida>⌋ and were originally coined to describe ways of translating the ⌊/⌊>Bible>⌋/⌋, but the two approaches are applicable to any translation.@@@@1@31@@oe@26-8-2013
1000008800740@unknown@formal@none@1@S@"Formal equivalence" equates to "⌊>metaphrase>⌋," and "dynamic equivalence"—to "⌊>paraphrase>⌋."@@@@1@9@@oe@26-8-2013
1000008800750@unknown@formal@none@1@S@"Dynamic equivalence" (or "⌊/functional/⌋ equivalence") conveys the essential ⌊/⌊>thought>⌋/⌋ expressed in a source text — if necessary, at the expense of ⌊>literal>⌋ity, original ⌊>sememe>⌋ and ⌊>word order>⌋, the source text's active vs. passive ⌊>voice>⌋, etc.@@@@1@35@@oe@26-8-2013
1000008800760@unknown@formal@none@1@S@By contrast, "formal equivalence" (sought via ⌊>"literal" translation>⌋) attempts to render the text "⌊>literal>⌋ly," or "word for word" (the latter expression being itself a word-for-word rendering of the ⌊>classical Latin>⌋ "⌊/verbum pro verbo/⌋") — if necessary, at the expense of features natural to the ⌊>target language>⌋.@@@@1@46@@oe@26-8-2013
1000008800770@unknown@formal@none@1@S@There is, however, ⌊∗⌊/no sharp boundary/⌋∗⌋ between dynamic and formal equivalence.@@@@1@11@@oe@26-8-2013
1000008800780@unknown@formal@none@1@S@On the contrary, they represent a ⌊/spectrum/⌋ of translation approaches.@@@@1@10@@oe@26-8-2013
1000008800790@unknown@formal@none@1@S@Each is used at various times and in various contexts by the same translator, and at various points within the same text — sometimes simultaneously.@@@@1@25@@oe@26-8-2013
1000008800800@unknown@formal@none@1@S@Competent translation entails the judicious blending of dynamic and formal ⌊>equivalents>⌋.@@@@1@11@@oe@26-8-2013
1000008800810@unknown@formal@none@1@S@⌊=Back-translation¦2=⌋@@@@1@1@@oe@26-8-2013
1000008800820@unknown@formal@none@1@S@If one text is a translation of another, a ⌊∗back-translation∗⌋ is a translation of the translated text back into the language of the original text, made without reference to the original text.@@@@1@32@@oe@26-8-2013
1000008800830@unknown@formal@none@1@S@In the context of ⌊>machine translation>⌋, this is also called a "⌊∗round-trip translation∗⌋."@@@@1@13@@oe@26-8-2013
1000008800840@unknown@formal@none@1@S@Comparison of a back-translation to the original text is sometimes used as a ⌊>quality check>⌋ on the original translation, but it is certainly far from infallible and the reliability of this technique has been disputed.@@@@1@35@@oe@26-8-2013
1000008800850@unknown@formal@none@1@S@⌊=Literary translation¦2=⌋@@@@1@2@@oe@26-8-2013
1000008800860@unknown@formal@none@1@S@Translation of ⌊>literary works>⌋ (⌊>novel>⌋s, ⌊>short stories>⌋, ⌊>plays>⌋, ⌊>poems>⌋, etc.) is considered a literary pursuit in its own right.@@@@1@19@@oe@26-8-2013
1000008800870@unknown@formal@none@1@S@Notable in ⌊>Canadian literature>⌋ ⌊/specifically/⌋ as translators are figures such as ⌊>Sheila Fischman>⌋, ⌊>Robert Dickson>⌋ and ⌊>Linda Gaboriau>⌋, and the ⌊>Governor General's Awards>⌋ present prizes for the year's best English-to-French and French-to-English literary translations.@@@@1@34@@oe@26-8-2013
1000008800880@unknown@formal@none@1@S@Other writers, among many who have made a name for themselves as literary translators, include ⌊>Vasily Zhukovsky>⌋, ⌊>Tadeusz Boy-Żeleński>⌋, ⌊>Vladimir Nabokov>⌋, ⌊>Jorge Luis Borges>⌋, ⌊>Robert Stiller>⌋ and ⌊>Haruki Murakami>⌋.@@@@1@29@@oe@26-8-2013
1000008800890@unknown@formal@none@1@S@⌊=History¦3=⌋@@@@1@1@@oe@26-8-2013
1000008800900@unknown@formal@none@1@S@The first important translation in the West was that of the ⌊/⌊>Septuagint>⌋/⌋, a collection of ⌊>Jew>⌋ish Scriptures translated into ⌊>Koine Greek>⌋ in ⌊>Alexandria>⌋ between the 3rd and 1st centuries BCE.@@@@1@30@@oe@26-8-2013
1000008800910@unknown@formal@none@1@S@The dispersed ⌊>Jew>⌋s had forgotten their ancestral language and needed Greek versions (translations) of their Scriptures.@@@@1@16@@oe@26-8-2013
1000008800920@unknown@formal@none@1@S@Throughout the ⌊>Middle Ages>⌋, ⌊>Latin>⌋ was the ⌊/⌊>lingua franca>⌋/⌋ of the western learned world.@@@@1@14@@oe@26-8-2013
1000008800930@unknown@formal@none@1@S@The 9th-century ⌊>Alfred the Great>⌋, king of ⌊>Wessex>⌋ in ⌊>England>⌋, was far ahead of his time in commissioning ⌊>vernacular>⌋ ⌊>Anglo-Saxon>⌋ translations of ⌊>Bede>⌋'s ⌊/⌊>Ecclesiastical History>⌋/⌋ and ⌊>Boethius>⌋' ⌊/⌊>Consolation of Philosophy>⌋/⌋.@@@@1@30@@oe@26-8-2013
1000008800940@unknown@formal@none@1@S@Meanwhile the ⌊>Christian Church>⌋ frowned on even partial adaptations of the standard ⌊>Latin>⌋ ⌊/⌊>Bible>⌋/⌋, ⌊>St. Jerome>⌋'s ⌊/⌊>Vulgate>⌋/⌋ of ca. 384 CE.@@@@1@21@@oe@26-8-2013
1000008800950@unknown@formal@none@1@S@In ⌊>Asia>⌋, the spread of ⌊>Buddhism>⌋ led to large-scale ongoing translation efforts spanning well over a thousand years.@@@@1@18@@oe@26-8-2013
1000008800960@unknown@formal@none@1@S@The ⌊>Tangut Empire>⌋ was especially efficient in such efforts; exploiting the then newly-invented ⌊>block printing>⌋, and with the full support of the government (contemporary sources describe the Emperor and his mother personally contributing to the translation effort, alongside sages of various nationalities), the Tanguts took mere decades to translate volumes that had taken the ⌊>Chinese>⌋ centuries to render.@@@@1@58@@oe@26-8-2013
1000008800970@unknown@formal@none@1@S@Large-scale efforts at translation were undertaken by the ⌊>Arabs>⌋.@@@@1@9@@oe@26-8-2013
1000008800980@unknown@formal@none@1@S@Having conquered the Greek world, they made ⌊>Arabic>⌋ versions of its philosophical and scientific works.@@@@1@15@@oe@26-8-2013
1000008800990@unknown@formal@none@1@S@During the ⌊>Middle Ages>⌋, some translations of these Arabic versions were made into Latin, chiefly at ⌊>Córdoba>⌋ in ⌊>Spain>⌋.@@@@1@19@@oe@26-8-2013
1000008801000@unknown@formal@none@1@S@Such Latin translations of Greek and original Arab works of scholarship and science would help advance the development of European ⌊>Scholasticism>⌋.@@@@1@21@@oe@26-8-2013
1000008801010@unknown@formal@none@1@S@The broad historic trends in Western translation practice may be illustrated on the example of translation into the ⌊>English language>⌋.@@@@1@20@@oe@26-8-2013
1000008801020@unknown@formal@none@1@S@The first fine translations into English were made by England's first great poet, the 14th-century ⌊>Geoffrey Chaucer>⌋, who adapted from the ⌊>Italian>⌋ of ⌊>Giovanni Boccaccio>⌋ in his own ⌊/⌊>Knight's Tale>⌋/⌋ and ⌊/⌊>Troilus and Criseyde>⌋/⌋; began a translation of the ⌊>French-language>⌋ ⌊/⌊>Roman de la Rose>⌋/⌋; and completed a translation of ⌊>Boethius>⌋ from the ⌊>Latin>⌋.@@@@1@53@@oe@26-8-2013
1000008801030@unknown@formal@none@1@S@Chaucer founded an English ⌊>poetic>⌋ tradition on ⌊/⌊>adaptation>⌋s/⌋ and translations from those earlier-established ⌊>literary language>⌋s.@@@@1@15@@oe@26-8-2013
1000008801040@unknown@formal@none@1@S@The first great English translation was the ⌊/⌊>Wycliffe Bible>⌋/⌋ (ca. 1382), which showed the weaknesses of an underdeveloped English ⌊>prose>⌋.@@@@1@20@@oe@26-8-2013
1000008801050@unknown@formal@none@1@S@Only at the end of the 15th century would the great age of English prose translation begin with ⌊>Thomas Malory>⌋'s ⌊/⌊>Le Morte Darthur>⌋/⌋—an adaptation of ⌊>Arthurian romance>⌋s so free that it can, in fact, hardly be called a true translation.@@@@1@40@@oe@26-8-2013
1000008801060@unknown@formal@none@1@S@The first great ⌊>Tudor>⌋ translations are, accordingly, the ⌊/⌊>Tyndale New Testament>⌋/⌋ (1525), which would influence the ⌊/⌊>Authorized Version>⌋/⌋ (1611), and ⌊>Lord Berners>⌋' version of ⌊>Jean Froissart>⌋'s ⌊/Chronicles/⌋ (1523–25).@@@@1@28@@oe@26-8-2013
1000008801070@unknown@formal@none@1@S@Meanwhile, in ⌊>Renaissance>⌋ ⌊>Italy>⌋, a new period in the history of translation had opened in ⌊>Florence>⌋ with the arrival, at the court of ⌊>Cosimo de' Medici>⌋, of the ⌊>Byzantine>⌋ scholar ⌊>Georgius Gemistus Pletho>⌋ shortly before the fall of ⌊>Constantinople>⌋ to the Turks (1453).@@@@1@43@@oe@26-8-2013
1000008801080@unknown@formal@none@1@S@A Latin translation of ⌊>Plato>⌋'s works was undertaken by ⌊>Marsilio Ficino>⌋.@@@@1@11@@oe@26-8-2013
1000008801090@unknown@formal@none@1@S@This and ⌊>Erasmus>⌋' Latin edition of the ⌊/⌊>New Testament>⌋/⌋ led to a new attitude to translation.@@@@1@16@@oe@26-8-2013
1000008801100@unknown@formal@none@1@S@For the first time, readers demanded rigor of rendering, as philosophical and religious beliefs depended on the exact words of ⌊>Plato>⌋, ⌊>Aristotle>⌋ and ⌊>Jesus>⌋.@@@@1@24@@oe@26-8-2013
1000008801110@unknown@formal@none@1@S@Non-scholarly literature, however, continued to rely on ⌊/adaptation/⌋.@@@@1@8@@oe@26-8-2013
1000008801120@unknown@formal@none@1@S@⌊>France>⌋'s ⌊/⌊>Pléiade>⌋/⌋, ⌊>England>⌋'s ⌊>Tudor>⌋ poets, and the ⌊>Elizabethan>⌋ translators adapted themes by ⌊>Horace>⌋, ⌊>Ovid>⌋, ⌊>Petrarch>⌋ and modern Latin writers, forming a new poetic style on those models.@@@@1@27@@oe@26-8-2013
1000008801130@unknown@formal@none@1@S@The English poets and translators sought to supply a new public, created by the rise of a ⌊>middle class>⌋ and the development of ⌊>printing>⌋, with works such as the original authors ⌊/would have written/⌋, had they been writing in England in that day.@@@@1@43@@oe@26-8-2013
1000008801140@unknown@formal@none@1@S@The ⌊>Elizabethan>⌋ period of translation saw considerable progress beyond mere ⌊>paraphrase>⌋ toward an ideal of ⌊>stylistic>⌋ equivalence, but even to the end of this period—which actually reached to the middle of the 17th century—there was no concern for ⌊>verbal>⌋ ⌊>accuracy>⌋.@@@@1@40@@oe@26-8-2013
1000008801150@unknown@formal@none@1@S@In the second half of the 17th century, the poet ⌊>John Dryden>⌋ sought to make ⌊>Virgil>⌋ speak "in words such as he would probably have written if he were living and an Englishman."@@@@1@33@@oe@26-8-2013
1000008801160@unknown@formal@none@1@S@Dryden, however, discerned no need to emulate the Roman poet's subtlety and concision.@@@@1@13@@oe@26-8-2013
1000008801170@unknown@formal@none@1@S@Similarly, ⌊>Homer>⌋ suffered from ⌊>Alexander Pope>⌋'s endeavor to reduce the Greek poet's "wild paradise" to order.@@@@1@16@@oe@26-8-2013
1000008801180@unknown@formal@none@1@S@Throughout the 18th century, the watchword of translators was ease of reading.@@@@1@12@@oe@26-8-2013
1000008801190@unknown@formal@none@1@S@Whatever they did not understand in a text, or thought might bore readers, they omitted.@@@@1@15@@oe@26-8-2013
1000008801200@unknown@formal@none@1@S@They cheerfully assumed that their own style of expression was the best, and that texts should be made to conform to it in translation.@@@@1@24@@oe@26-8-2013
1000008801210@unknown@formal@none@1@S@For scholarship they cared no more than had their predecessors, and they did not shrink from making translations from translations in third languages, or from languages that they hardly knew, or—as in the case of ⌊>James Macpherson>⌋'s "translations" of ⌊>Ossian>⌋—from texts that were actually of the "translator's" own composition.@@@@1@49@@oe@26-8-2013
1000008801220@unknown@formal@none@1@S@The 19th century brought new standards of accuracy and style.@@@@1@10@@oe@26-8-2013
1000008801230@unknown@formal@none@1@S@In regard to accuracy, observes J.M. Cohen, the policy became "the text, the whole text, and nothing but the text," except for any ⌊>bawdy>⌋ passages and the addition of copious explanatory ⌊>footnote>⌋s.@@@@1@32@@oe@26-8-2013
1000008801240@unknown@formal@none@1@S@In regard to style, the ⌊>Victorians>⌋' aim, achieved through far-reaching metaphrase (literality) or ⌊/pseudo/⌋-metaphrase, was to constantly remind readers that they were reading a ⌊/foreign/⌋ classic.@@@@1@26@@oe@26-8-2013
1000008801250@unknown@formal@none@1@S@An exception was the outstanding translation in this period, ⌊>Edward FitzGerald>⌋'s ⌊/⌊>Rubaiyat>⌋/⌋ of ⌊>Omar Khayyam>⌋ (1859), which achieved its Oriental flavor largely by using Persian names and discreet Biblical echoes and actually drew little of its material from the Persian original.@@@@1@41@@oe@26-8-2013
1000008801260@unknown@formal@none@1@S@In advance of the 20th century, a new pattern was set in 1871 by ⌊>Benjamin Jowett>⌋, who translated ⌊>Plato>⌋ into simple, straightforward language.@@@@1@23@@oe@26-8-2013
1000008801270@unknown@formal@none@1@S@Jowett's example was not followed, however, until well into the new century, when accuracy rather than style became the principal criterion.@@@@1@21@@oe@26-8-2013
1000008801280@unknown@formal@none@1@S@⌊=Poetry¦3=⌋@@@@1@1@@oe@26-8-2013
1000008801290@unknown@formal@none@1@S@⌊>Poetry>⌋ presents special challenges to translators, given the importance of a text's ⌊>form>⌋al aspects, in addition to its content.@@@@1@19@@oe@26-8-2013
1000008801300@unknown@formal@none@1@S@In his influential 1959 paper "On Linguistic Aspects of Translation," the ⌊>Russia>⌋n-born ⌊>linguist>⌋ and ⌊>semiotician>⌋ ⌊>Roman Jakobson>⌋ went so far as to declare that "poetry by definition [is] untranslatable."@@@@1@29@@oe@26-8-2013
1000008801310@unknown@formal@none@1@S@In 1974 the American poet ⌊>James Merrill>⌋ wrote a poem, "⌊>Lost in Translation>⌋," which in part explores this idea.@@@@1@19@@oe@26-8-2013
1000008801320@unknown@formal@none@1@S@The question was also discussed in ⌊>Douglas Hofstadter>⌋'s 1997 book, ⌊/⌊>Le Ton beau de Marot>⌋/⌋.@@@@1@15@@oe@26-8-2013
1000008801330@unknown@formal@none@1@S@⌊=Sung texts¦3=⌋@@@@1@2@@oe@26-8-2013
1000008801340@unknown@formal@none@1@S@Translation of a text that is sung in vocal music for the purpose of singing in another language — sometimes called "singing translation" — is closely linked to translation of poetry because most ⌊>vocal music>⌋, at least in the Western tradition, is set to ⌊>verse>⌋, especially verse in regular patterns with ⌊>rhyme>⌋.@@@@1@52@@oe@26-8-2013
1000008801350@unknown@formal@none@1@S@(Since the late 19th century, musical setting of ⌊>prose>⌋ and ⌊>free verse>⌋ has also been practiced in some ⌊>art music>⌋, though ⌊>popular music>⌋ tends to remain conservative in its retention of ⌊>stanza>⌋ic forms with or without ⌊>refrain>⌋s.)@@@@1@37@@oe@26-8-2013
1000008801360@unknown@formal@none@1@S@A rudimentary example of translating poetry for singing is church ⌊>hymn>⌋s, such as the German ⌊>chorale>⌋s translated into English by ⌊>Catherine Winkworth>⌋.@@@@1@22@@oe@26-8-2013
1000008801370@unknown@formal@none@1@S@Translation of sung texts is generally much more restrictive than translation of poetry, because in the former there is little or no freedom to choose between a versified translation and a translation that dispenses with verse structure.@@@@1@37@@oe@26-8-2013
1000008801380@unknown@formal@none@1@S@One might modify or omit rhyme in a singing translation, but the assignment of syllables to specific notes in the original musical setting places great challenges on the translator.@@@@1@29@@oe@26-8-2013
1000008801390@unknown@formal@none@1@S@There is the option in prose sung texts, less so in verse, of adding or deleting a syllable here and there by subdividing or combining notes, respectively, but even with prose the process is almost like strict verse translation because of the need to stick as closely as possible to the original prosody of the sung melodic line.@@@@1@58@@oe@26-8-2013
1000008801400@unknown@formal@none@1@S@Other considerations in writing a singing translation include repetition of words and phrases, the placement of rests and/or punctuation, the quality of vowels sung on high notes, and rhythmic features of the vocal line that may be more natural to the original language than to the target language.@@@@1@48@@oe@26-8-2013
1000008801410@unknown@formal@none@1@S@A sung translation may be considerably or completely different from the original, thus resulting in a ⌊>contrafactum>⌋.@@@@1@17@@oe@26-8-2013
1000008801420@unknown@formal@none@1@S@Translations of sung texts — whether of the above type meant to be sung or of a more or less literal type meant to be read — are also used as aids to audiences, singers and conductors, when a work is being sung in a language not known to them.@@@@1@50@@oe@26-8-2013
1000008801430@unknown@formal@none@1@S@The most familiar types are translations presented as subtitles projected during ⌊>opera>⌋ performances, those inserted into concert programs, and those that accompany commercial audio CDs of vocal music.@@@@1@28@@oe@26-8-2013
1000008801440@unknown@formal@none@1@S@In addition, professional and amateur singers often sing works in languages they do not know (or do not know well), and translations are then used to enable them to understand the meaning of the words they are singing.@@@@1@38@@oe@26-8-2013
1000008801450@unknown@formal@none@1@S@⌊=History of theory¦2=⌋@@@@1@3@@oe@26-8-2013
1000008801460@unknown@formal@none@1@S@Discussions of the theory and practice of translation reach back into ⌊>antiquity>⌋ and show remarkable ⌊>continuities>⌋.@@@@1@16@@oe@26-8-2013
1000008801470@unknown@formal@none@1@S@The distinction that had been drawn by the ⌊>ancient Greeks>⌋ between "⌊>metaphrase>⌋" ("literal" translation) and "⌊>paraphrase>⌋" would be adopted by the English ⌊>poet>⌋ and ⌊>translator>⌋ ⌊>John Dryden>⌋ (1631-1700), who represented translation as the judicious blending of these two modes of phrasing when selecting, in the target language, "counterparts," or ⌊>equivalents>⌋, for the expressions used in the source language:@@@@1@58@@oe@26-8-2013
1000008801480@unknown@formal@none@1@S@⌊"When [words] appear... literally graceful, it were an injury to the author that they should be changed.@@@@1@17@@oe@26-8-2013
1000008801490@unknown@formal@none@1@S@But since... what is beautiful in one [language] is often barbarous, nay sometimes nonsense, in another, it would be unreasonable to limit a translator to the narrow compass of his author's words: 'tis enough if he choose out some expression which does not vitiate the sense."⌋@@@@1@46@@oe@26-8-2013
1000008801500@unknown@formal@none@1@S@Dryden cautioned, however, against the license of "imitation," i.e. of adapted translation: "When a painter copies from the life... he has no privilege to alter features and lineaments..."@@@@1@28@@oe@26-8-2013
1000008801510@unknown@formal@none@1@S@This general formulation of the central concept of translation — ⌊>equivalence>⌋ — is probably as adequate as any that has been proposed ever since ⌊>Cicero>⌋ and ⌊>Horace>⌋, in first-century-BCE ⌊>Rome>⌋, famously and literally cautioned against translating "word for word" ("⌊/verbum pro verbo/⌋").@@@@1@42@@oe@26-8-2013
1000008801520@unknown@formal@none@1@S@Despite occasional theoretical diversities, the actual ⌊/practice/⌋ of translators has hardly changed since ⌊>antiquity>⌋.@@@@1@14@@oe@26-8-2013
1000008801530@unknown@formal@none@1@S@Except for some extreme ⌊>metaphrasers>⌋ in the early ⌊>Christian>⌋ period and the ⌊>Middle Ages>⌋, and adapters in various periods (especially pre-Classical Rome, and the 18th century), translators have generally shown prudent flexibility in seeking ⌊>equivalents>⌋ — "literal" where possible, ⌊>paraphrastic>⌋ where necessary — for the original ⌊>meaning>⌋ and other crucial "values" (e.g., style, ⌊>verse form>⌋, concordance with ⌊>music>⌋al accompaniment or, in ⌊>film>⌋s, with speech ⌊>articulatory>⌋ movements) as determined from context.@@@@1@70@@oe@26-8-2013
1000008801540@unknown@formal@none@1@S@In general, translators have sought to preserve the context itself by reproducing the original order of ⌊>sememe>⌋s, and hence ⌊>word order>⌋ — when necessary, reinterpreting the actual ⌊>grammatical>⌋ structure.@@@@1@29@@oe@26-8-2013
1000008801550@unknown@formal@none@1@S@The grammatical differences between "fixed-word-order" ⌊>language>⌋s (e.g., ⌊>English>⌋, ⌊>French>⌋, ⌊>German>⌋) and "free-word-order" languages (e.g., ⌊>Greek>⌋, ⌊>Latin>⌋, ⌊>Polish>⌋, ⌊>Russian>⌋) have been no impediment in this regard.@@@@1@25@@oe@26-8-2013
1000008801560@unknown@formal@none@1@S@When a target language has lacked ⌊>term>⌋s that are found in a source language, translators have borrowed them, thereby enriching the target language.@@@@1@23@@oe@26-8-2013
1000008801570@unknown@formal@none@1@S@Thanks in great measure to the exchange of "⌊/⌊>calque>⌋s/⌋" (French for "⌊>tracings>⌋") between languages, and to their importation from Greek, Latin, ⌊>Hebrew>⌋, ⌊>Arabic>⌋ and other languages, there are few ⌊>concept>⌋s that are "⌊>untranslatable>⌋" among the modern European languages.@@@@1@38@@oe@26-8-2013
1000008801580@unknown@formal@none@1@S@In general, the greater the contact and exchange that has existed between two languages, or between both and a third one, the greater is the ratio of ⌊>metaphrase>⌋ to ⌊>paraphrase>⌋ that may be used in translating between them.@@@@1@38@@oe@26-8-2013
1000008801590@unknown@formal@none@1@S@However, due to shifts in "⌊>ecological niche>⌋s" of words, a common ⌊>etymology>⌋ is sometimes misleading as a guide to current meaning in one or the other language.@@@@1@27@@oe@26-8-2013
1000008801600@unknown@formal@none@1@S@The ⌊>English>⌋ "actual," for example, should not be confused with the ⌊>cognate>⌋ ⌊>French>⌋ "⌊/actuel/⌋" (meaning "present," "current") or the ⌊>Polish>⌋ "⌊/aktualny/⌋" ("present," "current").@@@@1@23@@oe@26-8-2013
1000008801610@unknown@formal@none@1@S@For the translation of ⌊>Buddhist>⌋ texts into ⌊>Chinese>⌋, the monk ⌊>Xuanzang>⌋ (602–64) proposed the idea of 五不翻 ("five occasions when terms are left untranslated"):@@@@1@24@@oe@26-8-2013
1000008801620@unknown@formal@none@1@S@⌊•⌊#秘密故—terms carry secrecy, e.g., chants and spells;#⌋@@@@1@7@@oe@26-8-2013
1000008801630@unknown@formal@none@1@S@⌊#含多义故—terms carry multiple meanings;#⌋@@@@1@4@@oe@26-8-2013
1000008801640@unknown@formal@none@1@S@⌊#此无故—no corresponding term exists;#⌋@@@@1@4@@oe@26-8-2013
1000008801650@unknown@formal@none@1@S@⌊#顺古故—out of respect for earlier translations;#⌋@@@@1@6@@oe@26-8-2013
1000008801660@unknown@formal@none@1@S@⌊#生善故—#⌋•⌋@@@@1@1@@oe@26-8-2013
1000008801670@unknown@formal@none@1@S@The translator's role as a ⌊>bridge>⌋ for "carrying across" values between ⌊>culture>⌋s has been discussed at least since ⌊>Terence>⌋, Roman adapter of Greek comedies, in the second century BCE.@@@@1@29@@oe@26-8-2013
1000008801680@unknown@formal@none@1@S@The translator's role is, however, by no means a passive and mechanical one, and so has also been compared to that of an ⌊>artist>⌋.@@@@1@24@@oe@26-8-2013
1000008801690@unknown@formal@none@1@S@The main ground seems to be the concept of parallel creation found in critics as early as ⌊>Cicero>⌋.@@@@1@18@@oe@26-8-2013
1000008801700@unknown@formal@none@1@S@⌊>Dryden>⌋ observed that "Translation is a type of drawing after life..."@@@@1@11@@oe@26-8-2013
1000008801710@unknown@formal@none@1@S@Comparison of the translator with a ⌊>musician>⌋ or ⌊>actor>⌋ goes back at least to ⌊>Samuel Johnson>⌋'s remark about ⌊>Alexander Pope>⌋ playing ⌊>Homer>⌋ on a ⌊>flageolet>⌋, while Homer himself used a ⌊>bassoon>⌋.@@@@1@31@@oe@26-8-2013
1000008801720@unknown@formal@none@1@S@If translation be an art, it is no easy one.@@@@1@10@@oe@26-8-2013
1000008801730@unknown@formal@none@1@S@In the 13th century, ⌊>Roger Bacon>⌋ wrote that if a translation is to be true, the translator must know both ⌊>language>⌋s, as well as the ⌊>science>⌋ that he is to translate; and finding that few translators did, he wanted to do away with translation and translators altogether.@@@@1@47@@oe@26-8-2013
1000008801740@unknown@formal@none@1@S@The first ⌊>Europe>⌋an to assume that one translates satisfactorily only toward his own language may have been ⌊>Martin Luther>⌋, translator of the ⌊/⌊>Bible>⌋/⌋ into ⌊>German>⌋.@@@@1@25@@oe@26-8-2013
1000008801750@unknown@formal@none@1@S@According to L.G. Kelly, since ⌊>Johann Gottfried Herder>⌋ in the 18th century, "it has been axiomatic" that one works only toward his own language.@@@@1@24@@oe@26-8-2013
1000008801760@unknown@formal@none@1@S@Compounding these demands upon the translator is the fact that not even the most complete ⌊>dictionary>⌋ or ⌊>thesaurus>⌋ can ever be a fully adequate guide in translation.@@@@1@27@@oe@26-8-2013
1000008801770@unknown@formal@none@1@S@⌊>Alexander Tytler>⌋, in his ⌊/Essay on the Principles of Translation/⌋ (1790), emphasized that assiduous ⌊>reading>⌋ is a more comprehensive guide to a language than are dictionaries.@@@@1@26@@oe@26-8-2013
1000008801780@unknown@formal@none@1@S@The same point, but also including ⌊>listening>⌋ to the ⌊>spoken language>⌋, had earlier been made in 1783 by ⌊>Onufry Andrzej Kopczyński>⌋, member of ⌊>Poland>⌋'s Society for Elementary Books, who was called "the last Latin poet."@@@@1@35@@oe@26-8-2013
1000008801790@unknown@formal@none@1@S@The special role of the translator in society was well described in an essay, published posthumously in 1803, by ⌊>Ignacy Krasicki>⌋ — "Poland's ⌊>La Fontaine>⌋", ⌊>Primate of Poland>⌋, poet, encyclopedist, author of the first Polish novel, and translator from French and Greek:@@@@1@42@@oe@26-8-2013
1000008801800@unknown@formal@none@1@S@⌊=Religious texts¦2=⌋@@@@1@2@@oe@26-8-2013
1000008801810@unknown@formal@none@1@S@Translation of religious works has played an important role in history.@@@@1@11@@oe@26-8-2013
1000008801820@unknown@formal@none@1@S@Buddhist monks who translated the ⌊>India>⌋n ⌊>sutra>⌋s into ⌊>Chinese>⌋ often skewed their translations to better reflect ⌊>China>⌋'s very different ⌊>culture>⌋, emphasizing notions such as ⌊>filial piety>⌋.@@@@1@26@@oe@26-8-2013
1000008801830@unknown@formal@none@1@S@A famous mistranslation of the ⌊/⌊>Bible>⌋/⌋ is the rendering of the ⌊>Hebrew>⌋ word "⌊/keren/⌋," which has several meanings, as "horn" in a context where it actually means "beam of light."@@@@1@30@@oe@26-8-2013
1000008801840@unknown@formal@none@1@S@As a result, artists have for centuries depicted ⌊>Moses the Lawgiver>⌋ with horns growing out of his forehead.@@@@1@18@@oe@26-8-2013
1000008801850@unknown@formal@none@1@S@An example is ⌊>Michelangelo>⌋'s famous sculpture.@@@@1@6@@oe@26-8-2013
1000008801860@unknown@formal@none@1@S@⌊>Christian>⌋ ⌊>anti-Semite>⌋s used such depictions to spread hatred of the ⌊>Jews>⌋, claiming that they were ⌊>devil>⌋s with horns.@@@@1@18@@oe@26-8-2013
1000008801870@unknown@formal@none@1@S@One of the first recorded instances of translation in the West was the rendering of the ⌊>Old Testament>⌋ into ⌊>Greek>⌋ in the third century B.C.E.@@@@1@25@@oe@26-8-2013
1000008801880@unknown@formal@none@1@S@The resulting translation is known as the ⌊/⌊>Septuagint>⌋/⌋, a name that alludes to the "seventy" translators (seventy-two in some versions) who were commissioned to translate the ⌊/⌊>Bible>⌋/⌋ in ⌊>Alexandria>⌋.@@@@1@29@@oe@26-8-2013
1000008801890@unknown@formal@none@1@S@Each translator worked in solitary confinement in a separate cell, and legend has it that all seventy versions were identical.@@@@1@20@@oe@26-8-2013
1000008801900@unknown@formal@none@1@S@The ⌊/Septuagint/⌋ became the ⌊>source text>⌋ for later translations into many languages, including ⌊>Latin>⌋, ⌊>Coptic>⌋, ⌊>Armenian>⌋ and ⌊>Georgian>⌋.@@@@1@18@@oe@26-8-2013
1000008801910@unknown@formal@none@1@S@⌊>Saint Jerome>⌋, the ⌊>patron saint>⌋ of translation, is still considered one of the greatest translators in history for rendering the ⌊/⌊>Bible>⌋/⌋ into ⌊>Latin>⌋.@@@@1@23@@oe@26-8-2013
1000008801920@unknown@formal@none@1@S@The ⌊>Roman Catholic Church>⌋ used his translation (known as the ⌊>Vulgate>⌋) for centuries, but even this translation at first stirred much controversy.@@@@1@22@@oe@26-8-2013
1000008801930@unknown@formal@none@1@S@The period preceding and contemporary with the ⌊>Protestant Reformation>⌋ saw the translation of the ⌊/⌊>Bible>⌋/⌋ into local European languages, a development that greatly affected ⌊>Western Christianity>⌋'s split into ⌊>Roman Catholicism>⌋ and ⌊>Protestantism>⌋, due to disparities between Catholic and Protestant versions of crucial words and passages.@@@@1@45@@oe@26-8-2013
1000008801940@unknown@formal@none@1@S@⌊>Martin Luther>⌋'s ⌊/⌊>Bible>⌋/⌋ in ⌊>German>⌋, ⌊>Jakub Wujek>⌋'s in ⌊>Polish>⌋, and the ⌊/⌊>King James Bible>⌋/⌋ in ⌊>English>⌋ had lasting effects on the religions, cultures and languages of those countries.@@@@1@28@@oe@26-8-2013
1000008801950@unknown@formal@none@1@S@⌊=Machine translation¦2=⌋@@@@1@2@@oe@26-8-2013
1000008801960@unknown@formal@none@1@S@⌊>Machine translation>⌋ (MT) is a procedure whereby a computer program analyzes a ⌊>source text>⌋ and produces a target text ⌊/without further human intervention/⌋.@@@@1@23@@oe@26-8-2013
1000008801970@unknown@formal@none@1@S@In reality, however, machine translation typically ⌊/does/⌋ involve human intervention, in the form of ⌊∗pre-editing∗⌋ and ⌊∗post-editing∗⌋.@@@@1@17@@oe@26-8-2013
1000008801980@unknown@formal@none@1@S@An exception to that rule might be, e.g., the translation of technical specifications (strings of ⌊>technical terms>⌋ and adjectives), using a ⌊>dictionary-based machine-translation>⌋ system.@@@@1@24@@oe@26-8-2013
1000008801990@unknown@formal@none@1@S@To date, machine translation—a major goal of ⌊>natural-language processing>⌋—has met with limited success.@@@@1@13@@oe@26-8-2013
1000008802000@unknown@formal@none@1@S@A ⌊>November 6>⌋, ⌊>2007>⌋, example illustrates the hazards of uncritical reliance on ⌊>machine translation>⌋.@@@@1@14@@oe@26-8-2013
1000008802010@unknown@formal@none@1@S@Machine translation has been brought to a large public by tools available on the Internet, such as ⌊>Yahoo!>⌋'s ⌊>Babel Fish>⌋, ⌊>Babylon>⌋, and ⌊>StarDict>⌋.@@@@1@23@@oe@26-8-2013
1000008802020@unknown@formal@none@1@S@These tools produce a "gisting translation" — a rough translation that, with luck, "gives the gist" of the source text.@@@@1@20@@oe@26-8-2013
1000008802030@unknown@formal@none@1@S@With proper ⌊>terminology work>⌋, with preparation of the source text for machine translation (pre-editing), and with re-working of the machine translation by a professional human translator (post-editing), commercial machine-translation tools can produce useful results, especially if the machine-translation system is integrated with a ⌊>translation-memory>⌋ or ⌊>globalization-management system>⌋.@@@@1@47@@oe@26-8-2013
1000008802040@unknown@formal@none@1@S@In regard to texts (e.g., ⌊>weather reports>⌋) with limited ranges of ⌊>vocabulary>⌋ and simple ⌊>sentence>⌋ ⌊>structure>⌋, machine translation can deliver results that do not require much human intervention to be useful.@@@@1@31@@oe@26-8-2013
1000008802050@unknown@formal@none@1@S@Also, the use of a ⌊>controlled language>⌋, combined with a machine-translation tool, will typically generate largely comprehensible translations.@@@@1@18@@oe@26-8-2013
1000008802060@unknown@formal@none@1@S@Relying on machine translation exclusively ignores the fact that communication in ⌊>human language>⌋ is ⌊>context>⌋-embedded and that it takes a person to comprehend the context of the original text with a reasonable degree of probability.@@@@1@35@@oe@26-8-2013
1000008802070@unknown@formal@none@1@S@It is certainly true that even purely human-generated translations are prone to error.@@@@1@13@@oe@26-8-2013
1000008802080@unknown@formal@none@1@S@Therefore, to ensure that a machine-generated translation will be useful to a human being and that publishable-quality translation is achieved, such translations must be reviewed and edited by a human.@@@@1@30@@oe@26-8-2013
1000008802090@unknown@formal@none@1@S@⌊=CAT¦2=⌋@@@@1@1@@oe@26-8-2013
1000008802100@unknown@formal@none@1@S@⌊>Computer-assisted translation>⌋ (CAT), also called "computer-⌊/aided/⌋ translation," "machine-aided human translation (MAHT)" and "interactive translation," is a form of translation wherein a human translator creates a target text with the assistance of a computer program.@@@@1@34@@oe@26-8-2013
1000008802110@unknown@formal@none@1@S@The ⌊∗machine∗⌋ supports a human ⌊∗translator∗⌋.@@@@1@6@@oe@26-8-2013
1000008802120@unknown@formal@none@1@S@Computer-assisted translation can include standard ⌊>dictionary>⌋ and grammar software.@@@@1@9@@oe@26-8-2013
1000008802130@unknown@formal@none@1@S@The term, however, normally refers to a range of specialized programs available to the translator, including ⌊>translation-memory>⌋, ⌊>terminology-management>⌋, ⌊>concordance>⌋, and alignment programs.@@@@1@22@@oe@26-8-2013
1000008802140@unknown@formal@none@1@S@With the internet, translation software can help non-native-speaking individuals understand web pages published in other languages.@@@@1@16@@oe@26-8-2013
1000008802150@unknown@formal@none@1@S@Whole-page translation tools are of limited utility, however, since they offer only a limited potential understanding of the original author's intent and context; translated pages tend to be more humorous and confusing than enlightening.@@@@1@34@@oe@26-8-2013
1000008802160@unknown@formal@none@1@S@Interactive translations with pop-up windows are becoming more popular.@@@@1@9@@oe@26-8-2013
1000008802170@unknown@formal@none@1@S@These tools show several possible translations of each word or phrase.@@@@1@11@@oe@26-8-2013
1000008802180@unknown@formal@none@1@S@Human operators merely need to select the correct translation as the mouse glides over the foreign-language text.@@@@1@17@@oe@26-8-2013
1000008802190@unknown@formal@none@1@S@Possible definitions can be grouped by pronunciation.@@@@1@7@@oe@26-8-2013
1000008900010@unknown@formal@none@1@S@⌊δTranslation memoryδ⌋@@@@1@2@@oe@26-8-2013
1000008900020@unknown@formal@none@1@S@A ⌊∗translation memory∗⌋, or ⌊∗TM∗⌋, is a type of database that is used in software programs designed to aid human ⌊>translator>⌋s.@@@@1@21@@oe@26-8-2013
1000008900030@unknown@formal@none@1@S@Some software programs that use translation memories are known as ⌊∗translation memory managers∗⌋ (⌊∗TMM∗⌋).@@@@1@14@@oe@26-8-2013
1000008900040@unknown@formal@none@1@S@Translation memories are typically used in conjunction with a dedicated ⌊>computer assisted translation>⌋ (CAT) tool, ⌊>word processing>⌋ program, ⌊>terminology management systems>⌋, multilingual dictionary, or even raw ⌊>machine translation>⌋ output.@@@@1@29@@oe@26-8-2013
1000008900050@unknown@formal@none@1@S@A translation memory consists of text segments in a source language and their translations into one or more target languages.@@@@1@20@@oe@26-8-2013
1000008900060@unknown@formal@none@1@S@These segments can be blocks, paragraphs, sentences, or phrases.@@@@1@9@@oe@26-8-2013
1000008900070@unknown@formal@none@1@S@Individual words are handled by terminology bases and are not within the domain of TM.@@@@1@15@@oe@26-8-2013
1000008900080@unknown@formal@none@1@S@Research indicates that many companies producing multilingual documentation are using translation memory systems.@@@@1@13@@oe@26-8-2013
1000008900090@unknown@formal@none@1@S@In a survey of language professionals in 2006, 82.5 % out of 874 replies confirmed the use of a TM.@@@@1@20@@oe@26-8-2013
1000008900100@unknown@formal@none@1@S@Usage of TM correlated with text type characterised by technical terms and simple sentence structure (technical, to a lesser degree marketing and financial), computing skills, and repetitiveness of content@@@@1@29@@oe@26-8-2013
1000008900110@unknown@formal@none@1@S@⌊=Using translation memories¦2=⌋@@@@1@3@@oe@26-8-2013
1000008900120@unknown@formal@none@1@S@The program breaks the ⌊∗source text∗⌋ (the text to be translated) into segments, looks for matches between segments and the source half of previously translated source-target pairs stored in a ⌊∗translation memory∗⌋, and presents such matching pairs as translation ⌊∗candidates∗⌋.@@@@1@40@@oe@26-8-2013
1000008900130@unknown@formal@none@1@S@The translator can accept a candidate, replace it with a fresh translation, or modify it to match the source.@@@@1@19@@oe@26-8-2013
1000008900140@unknown@formal@none@1@S@In the last two cases, the new or modified translation goes into the database.@@@@1@14@@oe@26-8-2013
1000008900150@unknown@formal@none@1@S@Some translation memories systems search for 100% matches only, that is to say that they can only retrieve segments of text that match entries in the database exactly, while others employ ⌊>fuzzy matching>⌋ algorithms to retrieve similar segments, which are presented to the translator with differences flagged.@@@@1@47@@oe@26-8-2013
1000008900160@unknown@formal@none@1@S@It is important to note that typical translation memory systems only search for text in the source segment.@@@@1@18@@oe@26-8-2013
1000008900170@unknown@formal@none@1@S@The flexibility and robustness of the matching algorithm largely determine the performance of the translation memory, although for some applications the recall rate of exact matches can be high enough to justify the 100%-match approach.@@@@1@35@@oe@26-8-2013
1000008900180@unknown@formal@none@1@S@Segments where no match is found will have to be translated by the translator manually.@@@@1@15@@oe@26-8-2013
1000008900190@unknown@formal@none@1@S@These newly translated segments are stored in the database where they can be used for future translations as well as repetitions of that segment in the current text.@@@@1@28@@oe@26-8-2013
1000008900200@unknown@formal@none@1@S@Translation memories work best on texts which are highly repetitive, such as technical manuals.@@@@1@14@@oe@26-8-2013
1000008900210@unknown@formal@none@1@S@They are also helpful for translating incremental changes in a previously translated document, corresponding, for example, to minor changes in a new version of a user manual.@@@@1@27@@oe@26-8-2013
1000008900220@unknown@formal@none@1@S@Traditionally, translation memories have not been considered appropriate for literary or creative texts, for the simple reason that there is so little repetition in the language used.@@@@1@27@@oe@26-8-2013
1000008900230@unknown@formal@none@1@S@However, others find them of value even for non-repetitive texts, because the database resources created have value for concordance searches to determine appropriate usage of terms, for quality assurance (no empty segments), and the simplification of the review process (source and target segment are always displayed together while translators have to work with two documents in a traditional review environment).@@@@1@60@@oe@26-8-2013
1000008900240@unknown@formal@none@1@S@If a translation memory system is used consistently on appropriate texts over a period of time, it can save translators considerable work.@@@@1@22@@oe@26-8-2013
1000008900250@unknown@formal@none@1@S@⌊=Main benefits¦3=⌋@@@@1@2@@oe@26-8-2013
1000008900260@unknown@formal@none@1@S@Translation memory managers are most suitable for translating technical documentation and documents containing specialized vocabularies.@@@@1@15@@oe@26-8-2013
1000008900270@unknown@formal@none@1@S@Their benefits include:@@@@1@3@@oe@26-8-2013
1000008900280@unknown@formal@none@1@S@⌊•⌊#Ensuring that the document is completely translated (translation memories do not accept empty target segments)#⌋@@@@1@15@@oe@26-8-2013
1000008900290@unknown@formal@none@1@S@⌊#Ensuring that the translated documents are consistent, including common definitions, phrasings and terminology.@@@@1@13@@oe@26-8-2013
1000008900300@unknown@formal@none@1@S@This is important when different translators are working on a single project.#⌋@@@@1@12@@oe@26-8-2013
1000008900310@unknown@formal@none@1@S@⌊#Enabling translators to translate documents in a wide variety of formats without having to own the software typically required to process these formats.#⌋@@@@1@23@@oe@26-8-2013
1000008900320@unknown@formal@none@1@S@⌊#Accelerating the overall translation process; since translation memories "remember" previously translated material, translators have to translate it only once.#⌋@@@@1@19@@oe@26-8-2013
1000008900330@unknown@formal@none@1@S@⌊#Reducing costs of long-term translation projects; for example the text of manuals, warning messages or series of documents needs to be translated only once and can be used several times.#⌋@@@@1@30@@oe@26-8-2013
1000008900340@unknown@formal@none@1@S@⌊#For large documentation projects, savings (in time or money) thanks to the use of a TM package may already be apparent even for the first translation of a new project, but normally such savings are only apparent when translating subsequent versions of a project that was translated before using translation memory.#⌋•⌋@@@@1@51@@oe@26-8-2013
1000008900350@unknown@formal@none@1@S@⌊=Main obstacles¦3=⌋@@@@1@2@@oe@26-8-2013
1000008900360@unknown@formal@none@1@S@The main problems hindering wider use of translation memory managers include:@@@@1@11@@oe@26-8-2013
1000008900370@unknown@formal@none@1@S@⌊•⌊#The concept of "translation memories" is based on the premise that sentences used in previous translations can be "recycled".@@@@1@19@@oe@26-8-2013
1000008900380@unknown@formal@none@1@S@However, a guiding principle of translation is that the translator must translate the ⌊/message/⌋ of the text, and not its component ⌊/⌊>sentences>⌋/⌋.#⌋@@@@1@22@@oe@26-8-2013
1000008900390@unknown@formal@none@1@S@⌊#Translation memory managers do not easily fit into existing translation or localization processes.@@@@1@13@@oe@26-8-2013
1000008900400@unknown@formal@none@1@S@In order to take advantages of TM technology, the ⌊>translation process>⌋es must be redesigned.#⌋@@@@1@14@@oe@26-8-2013
1000008900410@unknown@formal@none@1@S@⌊#Translation memory managers do not presently support all documentation formats, and filters may not exist to support all file types.#⌋@@@@1@20@@oe@26-8-2013
1000008900420@unknown@formal@none@1@S@⌊#There is a learning curve associated with using translation memory managers, and the programs must be customized for greatest effectiveness.#⌋@@@@1@20@@oe@26-8-2013
1000008900430@unknown@formal@none@1@S@⌊#In cases where all or part of the translation process is outsourced or handled by freelance translators working off-site, the off-site workers require special tools to be able to work with the texts generated by the translation memory manager.#⌋@@@@1@39@@oe@26-8-2013
1000008900440@unknown@formal@none@1@S@⌊#Full versions of many translation memory managers can cost from ⌊>US$>⌋500 to US$2,500 per seat, which can represent a considerable investment (although lower cost programs are also available).@@@@1@28@@oe@26-8-2013
1000008900450@unknown@formal@none@1@S@However, some developers produce free or low-cost versions of their tools with reduced feature sets that individual translators can use to work on projects set up with full versions of those tools.@@@@1@32@@oe@26-8-2013
1000008900460@unknown@formal@none@1@S@(Note that there are freeware and shareware TM packages available, but none of these has yet gained a large market share.)#⌋@@@@1@21@@oe@26-8-2013
1000008900470@unknown@formal@none@1@S@⌊#The costs involved in importing the user's past translations into the translation memory database, training, as well as any add-on products may also represent a considerable investment.#⌋@@@@1@27@@oe@26-8-2013
1000008900480@unknown@formal@none@1@S@⌊#Maintenance of translation memory databases still tends to be a manual process in most cases, and failure to maintain them can result in significantly decreased usability and quality of TM matches.#⌋@@@@1@31@@oe@26-8-2013
1000008900490@unknown@formal@none@1@S@⌊#As stated previously, translation memory managers may not be suitable for text that lacks internal repetition or which does not contain unchanged portions between revisions.@@@@1@25@@oe@26-8-2013
1000008900500@unknown@formal@none@1@S@Technical text is generally best suited for translation memory, while marketing or creative texts will be less suitable.#⌋@@@@1@18@@oe@26-8-2013
1000008900510@unknown@formal@none@1@S@⌊#The quality of the text recorded in the translation memory is not guaranteed; if the translation for particular segment is incorrect, it is in fact more likely that the incorrect translation will be reused the next time the same source text, or a similar source text, is translated, thereby perpetuating the error.#⌋@@@@1@52@@oe@26-8-2013
1000008900520@unknown@formal@none@1@S@⌊#There is also a potential, and, if present, probably an unconscious effect on the translated text.@@@@1@16@@oe@26-8-2013
1000008900530@unknown@formal@none@1@S@Different languages use different sequences for the logical elements within a sentence and a translator presented with a multiple clause sentence that is half translated is less likely to completely rebuild a sentence.#⌋@@@@1@33@@oe@26-8-2013
1000008900540@unknown@formal@none@1@S@⌊#There is also a potential for the translator to deal with the text mechanically sentence-by-sentence, instead of focusing on how each sentence relates to those around it and to the text as a whole.#⌋@@@@1@34@@oe@26-8-2013
1000008900550@unknown@formal@none@1@S@⌊#Translation memories also raise certain industrial relations issues as they make exploitation of human translators easier.#⌋•⌋@@@@1@16@@oe@26-8-2013
1000008900560@unknown@formal@none@1@S@⌊=Functions of a translation memory¦2=⌋@@@@1@5@@oe@26-8-2013
1000008900570@unknown@formal@none@1@S@The following is a summary of the main functions of a Translation Memory.@@@@1@13@@oe@26-8-2013
1000008900580@unknown@formal@none@1@S@⌊=Off-line functions¦3=⌋@@@@1@2@@oe@26-8-2013
1000008900590@unknown@formal@none@1@S@⌊=Import¦4=⌋@@@@1@1@@oe@26-8-2013
1000008900600@unknown@formal@none@1@S@This function is used to transfer a text and its translation from a text file to the TM.@@@@1@18@@oe@26-8-2013
1000008900610@unknown@formal@none@1@S@⌊>Import>⌋ can be done from a ⌊/raw format/⌋, in which an external source text is available for importing into a TM along with its translation.@@@@1@25@@oe@26-8-2013
1000008900620@unknown@formal@none@1@S@Sometimes the texts have to be reprocessed by the user.@@@@1@10@@oe@26-8-2013
1000008900630@unknown@formal@none@1@S@There is another format that can be used to import: the ⌊/native format/⌋.@@@@1@13@@oe@26-8-2013
1000008900640@unknown@formal@none@1@S@This format is the one that uses the TM to save translation memories in a file.@@@@1@16@@oe@26-8-2013
1000008900650@unknown@formal@none@1@S@⌊=Analysis¦4=⌋@@@@1@1@@oe@26-8-2013
1000008900660@unknown@formal@none@1@S@The process of analysis is developed through the following steps:@@@@1@10@@oe@26-8-2013
1000008900670@unknown@formal@none@1@S@⌊:⌊∗Textual parsing∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008900680@unknown@formal@none@1@S@⌊⇥It is very important to recognize punctuation in order to distinguish for example the end of sentence from abbreviation.@@@@1@19@@oe@26-8-2013
1000008900690@unknown@formal@none@1@S@Thus, mark-up is a kind of pre-editing.@@@@1@7@@oe@26-8-2013
1000008900700@unknown@formal@none@1@S@Usually, the materials which have been processed through translators' aid programs contain mark-up, as the translation stage is embedded in a multilingual document production line.@@@@1@25@@oe@26-8-2013
1000008900710@unknown@formal@none@1@S@Other special text elements may be set off by mark-up.@@@@1@10@@oe@26-8-2013
1000008900720@unknown@formal@none@1@S@There are special elements which do not need to be translated, such as proper names and codes, while others may need to be converted to native format.⇥⌋@@@@1@27@@oe@26-8-2013
1000008900730@unknown@formal@none@1@S@⌊:⌊∗Linguistic parsing∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008900740@unknown@formal@none@1@S@⌊⇥The base form reduction is used to prepare lists of words and a text for automatic retrieval of terms from a term bank.@@@@1@23@@oe@26-8-2013
1000008900750@unknown@formal@none@1@S@On the other hand, syntactic parsing may be used to extract multi-word terms or phraseology from a source text.@@@@1@19@@oe@26-8-2013
1000008900760@unknown@formal@none@1@S@So parsing is used to normalise word order variation of phraseology, this is which words can form a phrase.⇥⌋@@@@1@19@@oe@26-8-2013
1000008900770@unknown@formal@none@1@S@⌊:⌊∗Segmentation∗⌋:⌋@@@@1@1@@oe@26-8-2013
1000008900780@unknown@formal@none@1@S@⌊⇥Its purpose is to choose the most useful translation units.@@@@1@10@@oe@26-8-2013
1000008900790@unknown@formal@none@1@S@Segmentation is like a type of parsing.@@@@1@7@@oe@26-8-2013
1000008900800@unknown@formal@none@1@S@It is done monolingually using superficial parsing and alignment is based on segmentation.@@@@1@13@@oe@26-8-2013
1000008900810@unknown@formal@none@1@S@If the translators correct the segmentations manually, later versions of the document will not find matches against the TM based on the corrected segmentation because the program will repeat its own errors.@@@@1@32@@oe@26-8-2013
1000008900820@unknown@formal@none@1@S@Translators usually proceed sentence by sentence, although the translation of one sentence may depend on the translation of the surrounding ones.⇥⌋@@@@1@21@@oe@26-8-2013
1000008900830@unknown@formal@none@1@S@⌊:⌊∗Alignment∗⌋:⌋@@@@1@1@@oe@26-8-2013
1000008900840@unknown@formal@none@1@S@⌊⇥It is the task of defining translation correspondences between source and target texts.@@@@1@13@@oe@26-8-2013
1000008900850@unknown@formal@none@1@S@There should be feedback from alignment to segmentation and a good alignment algorithm should be able to correct initial segmentation.⇥⌋@@@@1@20@@oe@26-8-2013
1000008900860@unknown@formal@none@1@S@⌊:⌊∗Term extraction∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008900870@unknown@formal@none@1@S@⌊⇥It can have as input a previous dictionary.@@@@1@8@@oe@26-8-2013
1000008900880@unknown@formal@none@1@S@Moreover, when extracting unknown terms, it can use parsing based on text statistics.@@@@1@13@@oe@26-8-2013
1000008900890@unknown@formal@none@1@S@These are used to estimate the amount of work involved in a translation job.@@@@1@14@@oe@26-8-2013
1000008900900@unknown@formal@none@1@S@This is very useful for planning and scheduling the work.@@@@1@10@@oe@26-8-2013
1000008900910@unknown@formal@none@1@S@Translation statistics usually count the words and estimate the amount of repetition in the text.⇥⌋@@@@1@15@@oe@26-8-2013
1000008900920@unknown@formal@none@1@S@⌊=Export¦4=⌋@@@@1@1@@oe@26-8-2013
1000008900930@unknown@formal@none@1@S@Export transfers the text from the TM into an external text file.@@@@1@12@@oe@26-8-2013
1000008900940@unknown@formal@none@1@S@Import and export should be inverses.@@@@1@6@@oe@26-8-2013
1000008900950@unknown@formal@none@1@S@⌊=Online functions¦3=⌋@@@@1@2@@oe@26-8-2013
1000008900960@unknown@formal@none@1@S@When translating, one of the main purposes of the TM is to retrieve the most useful matches in the memory so that the translator can choose the best one.@@@@1@29@@oe@26-8-2013
1000008900970@unknown@formal@none@1@S@The TM must show both the source and target text pointing out the identities and differences.@@@@1@16@@oe@26-8-2013
1000008900980@unknown@formal@none@1@S@⌊=Retrieval¦4=⌋@@@@1@1@@oe@26-8-2013
1000008900990@unknown@formal@none@1@S@It is possible to retrieve from the TM one or more types of matches.@@@@1@14@@oe@26-8-2013
1000008901000@unknown@formal@none@1@S@⌊:⌊∗Exact match∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008901010@unknown@formal@none@1@S@⌊⇥Exact matches appear when the match between the current source segment and the stored one has been a character by character match.@@@@1@22@@oe@26-8-2013
1000008901020@unknown@formal@none@1@S@When translating a sentence, an exact match means the same sentence has been translated before.@@@@1@15@@oe@26-8-2013
1000008901030@unknown@formal@none@1@S@Exact matches are also called "100% matches".⇥⌋@@@@1@7@@oe@26-8-2013
1000008901040@unknown@formal@none@1@S@⌊:⌊∗In Context Exact (ICE) match∗⌋:⌋@@@@1@5@@oe@26-8-2013
1000008901050@unknown@formal@none@1@S@⌊⇥An ICE match is an exact match that occurs in exactly the same context, that is, the same location in a paragraph.@@@@1@22@@oe@26-8-2013
1000008901060@unknown@formal@none@1@S@Context is often defined by the surrounding sentences and attributes such as document file name, date, and permissions.⇥⌋@@@@1@18@@oe@26-8-2013
1000008901070@unknown@formal@none@1@S@⌊:⌊∗Fuzzy match∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008901080@unknown@formal@none@1@S@⌊⇥When the match has not been exact, it is a "fuzzy" match.@@@@1@12@@oe@26-8-2013
1000008901090@unknown@formal@none@1@S@Some systems assign percentages to these kinds of matches, in which case a fuzzy match is greater than 0% and less than 100%.@@@@1@23@@oe@26-8-2013
1000008901100@unknown@formal@none@1@S@Those figures are not comparable across systems unless the method of scoring is specified.⇥⌋@@@@1@14@@oe@26-8-2013
1000008901110@unknown@formal@none@1@S@⌊:⌊∗Concordance∗⌋:⌋@@@@1@1@@oe@26-8-2013
1000008901120@unknown@formal@none@1@S@⌊⇥This feature allows translators to select one or more words in the source segment and the system retrieves segment pairs that match the search criteria.@@@@1@25@@oe@26-8-2013
1000008901130@unknown@formal@none@1@S@This feature is helpful for finding translations of terms and idioms in the absence of a terminology database.⇥⌋@@@@1@18@@oe@26-8-2013
1000008901140@unknown@formal@none@1@S@⌊=Updating¦4=⌋@@@@1@1@@oe@26-8-2013
1000008901150@unknown@formal@none@1@S@A TM is updated with a new translation when it has been accepted by the translator.@@@@1@16@@oe@26-8-2013
1000008901160@unknown@formal@none@1@S@As always in updating a database, there is the question what to do with the previous contents of the database.@@@@1@20@@oe@26-8-2013
1000008901170@unknown@formal@none@1@S@A TM can be modified by changing or deleting entries in the TM.@@@@1@13@@oe@26-8-2013
1000008901180@unknown@formal@none@1@S@Some systems allow translators to save multiple translations of the same source segment.@@@@1@13@@oe@26-8-2013
1000008901190@unknown@formal@none@1@S@⌊=Automatic translation¦4=⌋@@@@1@2@@oe@26-8-2013
1000008901200@unknown@formal@none@1@S@Translation memories can do retrieval and substitution automatically without the help of the translator.@@@@1@14@@oe@26-8-2013
1000008901210@unknown@formal@none@1@S@If so.@@@@1@2@@oe@26-8-2013
1000008901220@unknown@formal@none@1@S@⌊:⌊∗Automatic retrieval∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008901230@unknown@formal@none@1@S@⌊⇥A TM features automatic retrieval and evaluation of translation correspondences in a translator's workbench.⇥⌋@@@@1@14@@oe@26-8-2013
1000008901240@unknown@formal@none@1@S@⌊:⌊∗Automatic substitution∗⌋:⌋@@@@1@2@@oe@26-8-2013
1000008901250@unknown@formal@none@1@S@⌊⇥Exact matches come up in translating new versions of a document.@@@@1@11@@oe@26-8-2013
1000008901260@unknown@formal@none@1@S@During automatic substitution, the translator does check the translation against the original, so if there are any mistakes in the previous translation, they will carry over.⇥⌋@@@@1@26@@oe@26-8-2013
1000008901270@unknown@formal@none@1@S@⌊=Networking¦4=⌋@@@@1@1@@oe@26-8-2013
1000008901280@unknown@formal@none@1@S@When networking during the translation it is possible to translate a text efficiently together with a group of translators.@@@@1@19@@oe@26-8-2013
1000008901290@unknown@formal@none@1@S@This way, the translations entered by one translator are available to the others.@@@@1@13@@oe@26-8-2013
1000008901300@unknown@formal@none@1@S@Moreover, if translation memories are shared before the final translation, there is a chance that mistakes made by one translator will be corrected by other team members.@@@@1@27@@oe@26-8-2013
1000008901310@unknown@formal@none@1@S@⌊=Text memory¦3=⌋@@@@1@2@@oe@26-8-2013
1000008901320@unknown@formal@none@1@S@"Text memory" is the basis of the proposed ⌊> Lisa OSCAR xml:tm standard>⌋.@@@@1@13@@oe@26-8-2013
1000008901330@unknown@formal@none@1@S@Text memory comprises author memory and translation memory.@@@@1@8@@oe@26-8-2013
1000008901340@unknown@formal@none@1@S@⌊=History of translation memories¦2=⌋@@@@1@4@@oe@26-8-2013
1000008901350@unknown@formal@none@1@S@The concept behind translation memories is not recent — university research into the concept began in the late 1970s, and the earliest commercializations became available in the late 1980s — but they became commercially viable only in the late 1990s.@@@@1@40@@oe@26-8-2013
1000008901360@unknown@formal@none@1@S@Originally translation memory systems stored aligned source and target sentences in a database, from which they could be recalled during translation.@@@@1@21@@oe@26-8-2013
1000008901370@unknown@formal@none@1@S@The problem with this 'leveraged' approach is that there is no guarantee if the new source language sentence is from the same context as the original database sentence.@@@@1@28@@oe@26-8-2013
1000008901380@unknown@formal@none@1@S@Therefore all 'leveraged' matches require that a translator reviews the memory match for relevance in the new document.@@@@1@18@@oe@26-8-2013
1000008901390@unknown@formal@none@1@S@Although cheaper than outright translation, this review still carries a cost.@@@@1@11@@oe@26-8-2013
1000008901400@unknown@formal@none@1@S@⌊=Support for new languages¦2=⌋@@@@1@4@@oe@26-8-2013
1000008901410@unknown@formal@none@1@S@Translation memory tools from majority of the companies do not support many upcoming languages.@@@@1@14@@oe@26-8-2013
1000008901420@unknown@formal@none@1@S@Recently Asian countries like India also jumped in to language computing and there is high scope for Translation memories in such developing countries.@@@@1@23@@oe@26-8-2013
1000008901430@unknown@formal@none@1@S@As most of the CAT software companies are concentrating on legacy languages, nothing much is happening on Asian languages.@@@@1@19@@oe@26-8-2013
1000008901440@unknown@formal@none@1@S@⌊=Recent trends¦3=⌋@@@@1@2@@oe@26-8-2013
1000008901450@unknown@formal@none@1@S@One recent development is the concept of 'text memory' in contrast to translation memory (see ⌊> Translating XML Documents with xml:tm>⌋).@@@@1@21@@oe@26-8-2013
1000008901460@unknown@formal@none@1@S@This is also the basis of the proposed LISA OSCAR ⌊> xml:tm>⌋ standard.@@@@1@13@@oe@26-8-2013
1000008901470@unknown@formal@none@1@S@Text memory within xml:tm comprises 'author memory' and 'translation memory'.@@@@1@10@@oe@26-8-2013
1000008901480@unknown@formal@none@1@S@Author memory is used to keep track of changes during the authoring cycle.@@@@1@13@@oe@26-8-2013
1000008901490@unknown@formal@none@1@S@Translation memory uses the information from author memory to implement translation memory matching.@@@@1@13@@oe@26-8-2013
1000008901500@unknown@formal@none@1@S@Although primarily targeted at XML documents, xml:tm can be used on any document that can be converted to ⌊> XLIFF>⌋ format.@@@@1@21@@oe@26-8-2013
1000008901510@unknown@formal@none@1@S@⌊=Second generation translation memories¦3=⌋@@@@1@4@@oe@26-8-2013
1000008901520@unknown@formal@none@1@S@Much more powerful than first-generation TMs, they include a ⌊>linguistic analysis>⌋ engine, use chunk technology to break down segments into intelligent terminological groups, and automatically generate specific glossaries.@@@@1@28@@oe@26-8-2013
1000008901530@unknown@formal@none@1@S@⌊=Translation memory and related standards¦2=⌋@@@@1@5@@oe@26-8-2013
1000008901540@unknown@formal@none@1@S@⌊=TMX¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901550@unknown@formal@none@1@S@⌊>Translation Memory Exchange format>⌋.@@@@1@4@@oe@26-8-2013
1000008901560@unknown@formal@none@1@S@This standard enables the interchange of translation memories between translation suppliers.@@@@1@11@@oe@26-8-2013
1000008901570@unknown@formal@none@1@S@TMX has been adopted by the translation community as the best way of importing and exporting translation memories.@@@@1@18@@oe@26-8-2013
1000008901580@unknown@formal@none@1@S@The current version is 1.4b - it allows for the recreation of the original source and target documents from the TMX data.@@@@1@22@@oe@26-8-2013
1000008901590@unknown@formal@none@1@S@An updated version, 2.0, is due to be released in 2008.@@@@1@11@@oe@26-8-2013
1000008901600@unknown@formal@none@1@S@⌊=TBX¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901610@unknown@formal@none@1@S@⌊>Termbase Exchange format>⌋.@@@@1@3@@oe@26-8-2013
1000008901620@unknown@formal@none@1@S@This LISA standard, which is currently being revised and republished as ISO 30042, allows for the interchange of terminology data including detailed lexical information.@@@@1@24@@oe@26-8-2013
1000008901630@unknown@formal@none@1@S@The framework for TBX is provided by three ISO standards: ISO 12620, ISO 12200 and ISO 16642.@@@@1@17@@oe@26-8-2013
1000008901640@unknown@formal@none@1@S@ISO 12620 provides an inventory of well-defined “data categories” with standardized names that function as data element types or as predefined values.@@@@1@22@@oe@26-8-2013
1000008901650@unknown@formal@none@1@S@ISO 12200 (also known as MARTIF) provides the basis for the core structure of TBX.@@@@1@15@@oe@26-8-2013
1000008901660@unknown@formal@none@1@S@ISO 16642 (also known as Terminological Markup Framework) includes a structural metamodel for Terminology Markup Languages in general.@@@@1@18@@oe@26-8-2013
1000008901670@unknown@formal@none@1@S@⌊=SRX¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901680@unknown@formal@none@1@S@⌊>Segmentation Rules Exchange format>⌋.@@@@1@4@@oe@26-8-2013
1000008901690@unknown@formal@none@1@S@SRX is intended to enhance the TMX standard so that translation memory data that is exchanged between applications can be used more effectively.@@@@1@23@@oe@26-8-2013
1000008901700@unknown@formal@none@1@S@The ability to specify the segmentation rules that were used in the previous translation increases the leveraging that can be achieved.@@@@1@21@@oe@26-8-2013
1000008901710@unknown@formal@none@1@S@⌊=GMX¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901720@unknown@formal@none@1@S@⌊>GILT Metrics>⌋.@@@@1@2@@oe@26-8-2013
1000008901730@unknown@formal@none@1@S@GILT stands for (Globalization, Internationalization, Localization, and Translation).@@@@1@8@@oe@26-8-2013
1000008901740@unknown@formal@none@1@S@The GILT Metrics standard comprises three parts: GMX-V for volume metrics, GMX-C for complexity metrics and GMX-Q for quality metrics.@@@@1@20@@oe@26-8-2013
1000008901750@unknown@formal@none@1@S@The proposed GILT Metrics standard is tasked with quantifying the workload and quality requirements for any given GILT task.@@@@1@19@@oe@26-8-2013
1000008901760@unknown@formal@none@1@S@⌊=OLIF¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901770@unknown@formal@none@1@S@⌊>Open Lexicon Interchange Format>⌋.@@@@1@4@@oe@26-8-2013
1000008901780@unknown@formal@none@1@S@OLIF is an open, XML-compliant standard for the exchange of terminological and lexical data.@@@@1@14@@oe@26-8-2013
1000008901790@unknown@formal@none@1@S@Although originally intended as a means for the exchange of lexical data between proprietary machine translation lexicons, it has evolved into a more general standard for terminology exchange.@@@@1@28@@oe@26-8-2013
1000008901800@unknown@formal@none@1@S@⌊=XLIFF¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901810@unknown@formal@none@1@S@⌊>XML Localisation Interchange File Format>⌋.@@@@1@5@@oe@26-8-2013
1000008901820@unknown@formal@none@1@S@It is intended to provide a single interchange file format that can be understood by any localization provider.@@@@1@18@@oe@26-8-2013
1000008901830@unknown@formal@none@1@S@XLIFF is the preferred way of exchanging data in XML format in the translation industry.@@@@1@15@@oe@26-8-2013
1000008901840@unknown@formal@none@1@S@⌊=TransWS¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901850@unknown@formal@none@1@S@⌊>Translation Web Services>⌋.@@@@1@3@@oe@26-8-2013
1000008901860@unknown@formal@none@1@S@TransWS specifies the calls needed to use Web services for the submission and retrieval of files and messages relating to localization projects.@@@@1@22@@oe@26-8-2013
1000008901870@unknown@formal@none@1@S@It is intended as a detailed framework for the automation of much of the current localization process by the use of Web Services.@@@@1@23@@oe@26-8-2013
1000008901880@unknown@formal@none@1@S@⌊=⌊>xml:tm>⌋¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901890@unknown@formal@none@1@S@⌊>xml:tm>⌋@@@@1@1@@oe@26-8-2013
1000008901891@unknown@formal@none@1@S@This approach to translation memory is based on the concept of text memory which comprises author and translation memory.@@@@1@19@@oe@26-8-2013
1000008901892@unknown@formal@none@1@S@⌊>xml:tm>⌋ has been donated to Lisa OSCAR by ⌊> XML-INTL>⌋.@@@@1@10@@oe@26-8-2013
1000008901900@unknown@formal@none@1@S@⌊=PO¦3=⌋@@@@1@1@@oe@26-8-2013
1000008901910@unknown@formal@none@1@S@⌊>Gettext Portable Object format>⌋.@@@@1@4@@oe@26-8-2013
1000008901920@unknown@formal@none@1@S@Though often not regarded as a translation memory format, Gettext PO files are bilingual files that are also used in translation memory processes in the same way translation memories are used.@@@@1@31@@oe@26-8-2013
1000008901930@unknown@formal@none@1@S@Typically, a PO translsation memory system will consist of various separate files in a director tree structure.@@@@1@17@@oe@26-8-2013
1000008901940@unknown@formal@none@1@S@Common tools that work with PO files include the ⌊> GNU Gettext Tools>⌋ and the ⌊> Translate Toolkit>⌋.@@@@1@18@@oe@26-8-2013
1000008901950@unknown@formal@none@1@S@Several tools and programs also exist that edit PO files as if they are mere source text files.@@@@1@18@@oe@26-8-2013
1000009000010@unknown@formal@none@1@S@⌊δTuring testδ⌋@@@@1@2@@oe@26-8-2013
1000009000020@unknown@formal@none@1@S@The ⌊∗Turing test∗⌋ is a proposal for a test of a ⌊>machine>⌋'s capability to demonstrate intelligence.@@@@1@16@@oe@26-8-2013
1000009000030@unknown@formal@none@1@S@Described by ⌊>Alan Turing>⌋ in the 1950 paper "⌊>Computing Machinery and Intelligence>⌋," it proceeds as follows: a human judge engages in a natural language conversation with one human and one machine, each of which try to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test.@@@@1@56@@oe@26-8-2013
1000009000040@unknown@formal@none@1@S@In order to test the machine's intelligence rather than its ability to render words into audio, the conversation is limited to a text-only channel such as a computer keyboard and screen (Turing originally suggested a ⌊>teletype machine>⌋, one of the few text-only communication systems available in 1950).@@@@1@47@@oe@26-8-2013
1000009000050@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000009000060@unknown@formal@none@1@S@While the field of ⌊>artificial intelligence>⌋ is said to have been founded in 1956, its roots extend back considerably further.@@@@1@20@@oe@26-8-2013
1000009000070@unknown@formal@none@1@S@The question as to whether or not it is possible for machines to think has a long history, firmly entrenched in the distinction between ⌊>dualist>⌋ and ⌊>materialist>⌋ views of the mind.@@@@1@31@@oe@26-8-2013
1000009000080@unknown@formal@none@1@S@From the perspective of dualism, the ⌊>mind>⌋ is ⌊>non-physical>⌋ (or, at the very least, has ⌊>non-physical properties>⌋), and therefore cannot be explained in purely physical terms.@@@@1@26@@oe@26-8-2013
1000009000090@unknown@formal@none@1@S@The materialist perspective, on the other hand, argues that the mind can be explained physically, and thus leaves open the possibility of minds that are artificially produced.@@@@1@27@@oe@26-8-2013
1000009000100@unknown@formal@none@1@S@⌊=Alan Turing¦3=⌋@@@@1@2@@oe@26-8-2013
1000009000110@unknown@formal@none@1@S@In more practical terms, researchers in Britain had been exploring "machine intelligence" for up to ten years prior to 1956.@@@@1@20@@oe@26-8-2013
1000009000120@unknown@formal@none@1@S@Alan Turing in particular had been tackling the notion of machine intelligence since at least 1941, and one of the earliest known mentions of "computer intelligence" was made by Turing in 1947.@@@@1@32@@oe@26-8-2013
1000009000130@unknown@formal@none@1@S@In Turing's report, "Intelligent Machinery", he investigated "the question of whether or not it is possible for machinery to show intelligent behaviour", and as part of that investigation proposed what may be considered the forerunner to his later tests:@@@@1@39@@oe@26-8-2013
1000009000140@unknown@formal@none@1@S@⌊""It is not difficult to devise a paper machine which will play a not very bad game of chess.@@@@1@19@@oe@26-8-2013
1000009000150@unknown@formal@none@1@S@Now get three men as subjects for the experiment.@@@@1@9@@oe@26-8-2013
1000009000151@unknown@formal@none@1@S@A, B and C.@@@@1@4@@oe@26-8-2013
1000009000152@unknown@formal@none@1@S@A and C are to be rather poor chess players, B is the operator who works the paper machine. ...@@@@1@20@@oe@26-8-2013
1000009000160@unknown@formal@none@1@S@Two rooms are used with some arrangement for communicating moves, and a game is played between C and either A or the paper machine.@@@@1@24@@oe@26-8-2013
1000009000170@unknown@formal@none@1@S@C may find it quite difficult to tell which he is playing.""⌋@@@@1@12@@oe@26-8-2013
1000009000180@unknown@formal@none@1@S@⌊λ⌊>Turing 1948>⌋, p. 431¦Turing¦1948¦p=431¦Harvard citation no bracketsλ⌋@@@@1@6@@oe@26-8-2013
1000009000190@unknown@formal@none@1@S@Thus by the time Turing published "Computing Machinery and Intelligence", he had been considering the possibility of machine intelligence for many years.@@@@1@22@@oe@26-8-2013
1000009000200@unknown@formal@none@1@S@This, however, was the first published paper by Turing to focus exclusively on the notion.@@@@1@15@@oe@26-8-2013
1000009000210@unknown@formal@none@1@S@Turing began his 1950 paper with the claim: "I propose to consider the question, 'Can machines think?'"@@@@1@17@@oe@26-8-2013
1000009000220@unknown@formal@none@1@S@As Turing highlighted, the traditional approach to such a question is to start with definitions, defining both the terms ⌊>machine>⌋ and ⌊>intelligence>⌋.@@@@1@22@@oe@26-8-2013
1000009000230@unknown@formal@none@1@S@Nevertheless, Turing chose not to do so.@@@@1@7@@oe@26-8-2013
1000009000240@unknown@formal@none@1@S@Instead he replaced the question with a new question, "which is closely related to it and is expressed in relatively unambiguous words".@@@@1@22@@oe@26-8-2013
1000009000250@unknown@formal@none@1@S@In essence, Turing proposed to change the question from "Do machines think?" into "Can machines do what we (as thinking entities) can do?"@@@@1@23@@oe@26-8-2013
1000009000260@unknown@formal@none@1@S@The advantage of the new question, Turing argued, was that it "drew a fairly sharp line between the physical and intellectual capacities of a man.@@@@1@25@@oe@26-8-2013
1000009000270@unknown@formal@none@1@S@To demonstrate this approach, Turing proposed a test that was inspired by a ⌊>party game>⌋ known as the "Imitation Game", in which a man and a woman go into separate rooms, and guests try to tell them apart by writing a series of questions and reading the typewritten answers sent back.@@@@1@51@@oe@26-8-2013
1000009000280@unknown@formal@none@1@S@In this game, both the man and the woman aim to convince the guests that they are the other.@@@@1@19@@oe@26-8-2013
1000009000290@unknown@formal@none@1@S@Turing proposed recreating the imitation game as follows:@@@@1@8@@oe@26-8-2013
1000009000300@unknown@formal@none@1@S@⌊""We now ask the question, 'What will happen when a machine takes the part of A in this game?'@@@@1@19@@oe@26-8-2013
1000009000310@unknown@formal@none@1@S@Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman?@@@@1@28@@oe@26-8-2013
1000009000320@unknown@formal@none@1@S@These questions replace our original, 'Can machines think?'"@@@@1@8@@oe@26-8-2013
1000009000330@unknown@formal@none@1@S@⌊"⌊λ⌊>Turing 1950>⌋, p. 434¦Turing¦1950¦p=434¦Harvard citation no bracketsλ⌋@@@@1@6@@oe@26-8-2013
1000009000340@unknown@formal@none@1@S@Later in the paper he suggested an "equivalent" alternative formulation involving a judge conversing only with a computer and a man.@@@@1@21@@oe@26-8-2013
1000009000350@unknown@formal@none@1@S@While neither of these two formulations precisely match the version of the Turing Test that is more generally known today, a third version was proposed by Turing in 1952.@@@@1@29@@oe@26-8-2013
1000009000360@unknown@formal@none@1@S@In this version, which Turing discussed in a ⌊>BBC>⌋ radio broadcast, Turing proposes a jury which asks questions of a computer, and where the role of the computer is to make a significant proportion of the jury believe that it is really a man.@@@@1@44@@oe@26-8-2013
1000009000370@unknown@formal@none@1@S@Turing's paper considered nine common objections, which include all the major arguments against artificial intelligence that have been raised in the years since his paper was first published.@@@@1@28@@oe@26-8-2013
1000009000380@unknown@formal@none@1@S@(See ⌊/⌊>Computing Machinery and Intelligence>⌋/⌋.)"⌋"⌋@@@@1@5@@oe@26-8-2013
1000009000390@unknown@formal@none@1@S@⌊=ELIZA, PARRY and the Chinese room¦3=⌋@@@@1@6@@oe@26-8-2013
1000009000400@unknown@formal@none@1@S@Blay Whitby lists four major turning points in the history of the Turing Test: the publication of "Computing Machinery and Intelligence" in 1950; the announcement of ⌊>Joseph Weizenbaum>⌋'s ⌊>ELIZA>⌋ in 1966; Kenneth Colby's creation of ⌊>PARRY>⌋, which was first described in 1972; and the Turing Colloquium in 1990.@@@@1@48@@oe@26-8-2013
1000009000410@unknown@formal@none@1@S@ELIZA works by examining a user's typed comments for keywords.@@@@1@10@@oe@26-8-2013
1000009000420@unknown@formal@none@1@S@If a word is found a rule is applied which transforms the user's comments, and the resulting sentence is then returned.@@@@1@21@@oe@26-8-2013
1000009000430@unknown@formal@none@1@S@If a keyword is not found, ELIZA responds with either a generic response or by repeating one of the earlier comments.@@@@1@21@@oe@26-8-2013
1000009000440@unknown@formal@none@1@S@In addition, Weizenbaum developed ELIZA to replicate the behavior of a ⌊>Rogerian psychotherapist>⌋, allowing ELIZA to be "free to assume the pose of knowing almost nothing of the real world."@@@@1@30@@oe@26-8-2013
1000009000450@unknown@formal@none@1@S@Due to these techniques, Weizenbaum's program was able to fool some people into believing that they were talking to a real person, with some subjects being "very hard to convince that ELIZA ... is ⌊/not/⌋ human."@@@@1@36@@oe@26-8-2013
1000009000460@unknown@formal@none@1@S@Thus ELIZA is claimed by many to be one of the programs (perhaps the first) that are able to pass the Turing Test.@@@@1@23@@oe@26-8-2013
1000009000470@unknown@formal@none@1@S@Colby's PARRY has been described as "ELIZA with attitude" - it attempts to model the behavior of a ⌊>paranoid>⌋ ⌊>schizophrenic>⌋, using a similar (if more advanced) approach to that employed by Weizenbaum.@@@@1@32@@oe@26-8-2013
1000009000480@unknown@formal@none@1@S@In order to help validate the work, PARRY was tested in the early 1970s using a variation of the Turing Test.@@@@1@21@@oe@26-8-2013
1000009000490@unknown@formal@none@1@S@A group of experienced psychiatrists analyzed a combination of real patients and computers running PARRY through ⌊>teletype>⌋ machines.@@@@1@18@@oe@26-8-2013
1000009000500@unknown@formal@none@1@S@Another group of 33 psychiatrists were shown transcripts of the conversations.@@@@1@11@@oe@26-8-2013
1000009000510@unknown@formal@none@1@S@The two groups were then asked to identify which of the "patients" were human, and which were computer programs.@@@@1@19@@oe@26-8-2013
1000009000520@unknown@formal@none@1@S@The psychiatrists were only able to make the correct identification 48% of the time - a figure consistent with random guessing.@@@@1@21@@oe@26-8-2013
1000009000530@unknown@formal@none@1@S@While neither ELIZA nor PARRY were able to pass a strict Turing Test, they - and software like them - suggested that software might be written that was able to do so.@@@@1@32@@oe@26-8-2013
1000009000540@unknown@formal@none@1@S@More importantly, they suggested that such software might involve little more than databases and the application of simple rules.@@@@1@19@@oe@26-8-2013
1000009000550@unknown@formal@none@1@S@This led to ⌊>John Searle>⌋'s 1980 paper, "Minds, Brains, and Programs", in which he proposed an argument against the Turing Test.@@@@1@21@@oe@26-8-2013
1000009000560@unknown@formal@none@1@S@Searle described a ⌊>thought experiment>⌋ known as the ⌊>Chinese room>⌋ that highlighted what he saw as a fundamental misinterpretation of what the Turing Test could and could not prove: while software such as ELIZA might be able to pass the Turing Test, they might do so by simply manipulating symbols of which they have no understanding.@@@@1@56@@oe@26-8-2013
1000009000570@unknown@formal@none@1@S@And without understanding, they could not be described as "thinking" in the same sense people do.@@@@1@16@@oe@26-8-2013
1000009000580@unknown@formal@none@1@S@Searle concludes that the Turing Test can not prove that a machine can think, contrary to Turing's original proposal.@@@@1@19@@oe@26-8-2013
1000009000590@unknown@formal@none@1@S@Arguments such as that proposed by Searle and others working in the ⌊>philosophy of mind>⌋ sparked off a more intense debate about the nature of intelligence, the possibility of intelligent machines and the value of the Turing test that continued through the 1980s and 1990s.@@@@1@45@@oe@26-8-2013
1000009000600@unknown@formal@none@1@S@⌊=1990s and beyond¦3=⌋@@@@1@3@@oe@26-8-2013
1000009000610@unknown@formal@none@1@S@1990 was the 40th anniversary of the first publication of Turing's "Computing Machinery and Intelligence" paper, and thus saw renewed interest in the test.@@@@1@24@@oe@26-8-2013
1000009000620@unknown@formal@none@1@S@Two significant events occurred in that year.@@@@1@7@@oe@26-8-2013
1000009000630@unknown@formal@none@1@S@The first with the Turing Colloquium, which was held at the ⌊>University of Sussex>⌋ in April, and brought together academics and researchers from a wide variety of disciplines to discuss the Turing Test in terms of its past, present and future.@@@@1@41@@oe@26-8-2013
1000009000640@unknown@formal@none@1@S@The second significant event was the formation of the annual ⌊>Loebner prize>⌋ competition.@@@@1@13@@oe@26-8-2013
1000009000650@unknown@formal@none@1@S@The Loebner prize was instigated by ⌊>Hugh Loebner>⌋ under the auspices of the Cambridge Center for Behavioral Studies of ⌊>Massachusetts>⌋, ⌊>United States>⌋, with the first competition held in November, 1991.@@@@1@30@@oe@26-8-2013
1000009000660@unknown@formal@none@1@S@As Loebner describes it, the competition was created to advance the state of AI research, at least in part because while the Turing Test had been discussed for many years, "no one had taken steps to implement it."@@@@1@38@@oe@26-8-2013
1000009000670@unknown@formal@none@1@S@The Loebner prize has three awards: the first prize of $100,000 and a gold medal, to be awarded to the first program that passes the "unrestricted" Turing test; the second prize of $25,000, to be awarded to the first program that passes the "restricted" version of the test; and a sum of $2000 (now $3000) to the "most human-like" program that was entered each year.@@@@1@65@@oe@26-8-2013
1000009000680@unknown@formal@none@1@S@⌊>As of 2007>⌋, neither the first nor second prizes have been awarded.@@@@1@12@@oe@26-8-2013
1000009000690@unknown@formal@none@1@S@The running of the Loebner prize led to renewed discussion of both the viability of the Turing Test and the aim of developing artificial intelligences that could pass it.@@@@1@29@@oe@26-8-2013
1000009000700@unknown@formal@none@1@S@⌊/⌊>The Economist>⌋/⌋, in an article entitled "Artificial Stupidity", commented that the winning entry from the first Loebner prize won, at least in part, because it was able to "imitate human typing errors".@@@@1@32@@oe@26-8-2013
1000009000710@unknown@formal@none@1@S@(Turing had considered the possibility that computers could be identified by their ⌊/lack/⌋ of errors, and had suggested that the computers should be programmed to add errors into their output, so as to be better "players" of the game).@@@@1@39@@oe@26-8-2013
1000009000720@unknown@formal@none@1@S@The issue that ⌊/The Economist/⌋ raised was one that was already well established in the literature: perhaps we don't really ⌊/need/⌋ the types of computers that could pass the Turing Test, and perhaps trying to pass the Turing Test is nothing more than a distraction from more fruitful lines of research.@@@@1@51@@oe@26-8-2013
1000009000730@unknown@formal@none@1@S@Equally, a second issue became apparent - by providing rules which restricted the abilities of the interrogators to ask questions, and by using comparatively "unsophisticated" interrogators, the Turing Test can be passed through the use of "trickery" rather than intelligence.@@@@1@40@@oe@26-8-2013
1000009000740@unknown@formal@none@1@S@⌊=Versions of the Turing test¦2=⌋@@@@1@5@@oe@26-8-2013
1000009000750@unknown@formal@none@1@S@There are at least three primary versions of the Turing test - two offered by Turing in "Computing Machinery and Intelligence" and one which Saul Traiger describes as the "Standard Interpretation".@@@@1@31@@oe@26-8-2013
1000009000760@unknown@formal@none@1@S@While there is some debate as to whether or not the "Standard Interpretation" is described by Turing or is, instead, based on a misreading of his paper, these three versions are not regarded as being equivalent, and are seen as having different strengths and weaknesses.@@@@1@45@@oe@26-8-2013
1000009000770@unknown@formal@none@1@S@As ⌊>empirical>⌋ tests they conform to a proposal published in 1936 by ⌊>A J Ayer>⌋ on how to distinguish between a conscious man and an unconscious machine.@@@@1@27@@oe@26-8-2013
1000009000780@unknown@formal@none@1@S@In his book ⌊/⌊>Language, Truth and Logic>⌋/⌋ Ayer states that 'The only ground I can have for asserting that an object which appears to be conscious is not really a conscious being, but only a dummy or a machine, is that it fails to satisfy one of the empirical tests by which the presence or absence of consciousness is determined'.@@@@1@60@@oe@26-8-2013
1000009000790@unknown@formal@none@1@S@⌊=The imitation game¦3=⌋@@@@1@3@@oe@26-8-2013
1000009000800@unknown@formal@none@1@S@Turing described a simple party game which involves three players.@@@@1@10@@oe@26-8-2013
1000009000810@unknown@formal@none@1@S@Player A is a man, player B is a woman, and player C (who plays the role of the interrogator) can be of either gender.@@@@1@25@@oe@26-8-2013
1000009000820@unknown@formal@none@1@S@In the imitation game, player C - the interrogator - is unable to see either player A or player B, and can only communicate with them through written notes.@@@@1@29@@oe@26-8-2013
1000009000830@unknown@formal@none@1@S@By asking questions of player A and player B, player C tries to determine which of the two is the man, and which of the two is the woman.@@@@1@29@@oe@26-8-2013
1000009000840@unknown@formal@none@1@S@Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator.@@@@1@21@@oe@26-8-2013
1000009000850@unknown@formal@none@1@S@In what Sterret refers to as the "Original Imitation Game Test", Turing proposed that the role of player A be replaced with a computer.@@@@1@24@@oe@26-8-2013
1000009000860@unknown@formal@none@1@S@The computer's task is therefore to pretend to be a woman and to attempt to trick the interrogator into making an incorrect evaluation.@@@@1@23@@oe@26-8-2013
1000009000870@unknown@formal@none@1@S@The success of the computer is determined by comparing the outcome of the game when player A is a computer against the outcome when player A is a man.@@@@1@29@@oe@26-8-2013
1000009000880@unknown@formal@none@1@S@If, as Turing puts it, "the interrogator decide[s] wrongly as often when the game is played [with the computer] as he does when the game is played between a man and a woman", then it can be argued that the computer is intelligent.@@@@1@43@@oe@26-8-2013
1000009000890@unknown@formal@none@1@S@The second version comes later in Turing's 1950 paper.@@@@1@9@@oe@26-8-2013
1000009000900@unknown@formal@none@1@S@As with the Original Imitation Game Test, the role of player A is performed by a computer.@@@@1@17@@oe@26-8-2013
1000009000910@unknown@formal@none@1@S@The difference is that now the role of player B is to be performed by a man, rather than by a woman.@@@@1@22@@oe@26-8-2013
1000009000920@unknown@formal@none@1@S@⌊""Let us fix our attention on one particular digital computer ⌊/C/⌋.@@@@1@11@@oe@26-8-2013
1000009000930@unknown@formal@none@1@S@Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, ⌊/C/⌋ can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?""⌋@@@@1@50@@oe@26-8-2013
1000009000940@unknown@formal@none@1@S@⌊λ⌊>Turing 1950>⌋, p. 442¦Turing¦1950¦p=442¦Harvard citation no bracketsλ⌋@@@@1@6@@oe@26-8-2013
1000009000950@unknown@formal@none@1@S@In this version both player A (the computer) and player B are trying to trick the interrogator into making an incorrect decision.@@@@1@22@@oe@26-8-2013
1000009000960@unknown@formal@none@1@S@⌊=The standard interpretation¦3=⌋@@@@1@3@@oe@26-8-2013
1000009000970@unknown@formal@none@1@S@A common understanding of the Turing test is that the purpose was not specifically to test if a computer is able to fool an interrogator into believing that it is a woman, but to test whether or not a computer could ⌊/imitate/⌋ a human.@@@@1@44@@oe@26-8-2013
1000009000980@unknown@formal@none@1@S@While there is some dispute as to whether or not this interpretation was intended by Turing (for example, Sterrett believes that it was, and thus conflates the second version with this one, while others, such as Traiger, do not), this has nevertheless led to what can be viewed as the "standard interpretation".@@@@1@52@@oe@26-8-2013
1000009000990@unknown@formal@none@1@S@In this version, player A is a computer, and player B is a person of either gender.@@@@1@17@@oe@26-8-2013
1000009001000@unknown@formal@none@1@S@The role of the interrogator is not to determine which is male and which is female, but to determine which is a computer and which is a human.@@@@1@28@@oe@26-8-2013
1000009001010@unknown@formal@none@1@S@⌊=Imitation game vs. standard Turing test¦3=⌋@@@@1@6@@oe@26-8-2013
1000009001020@unknown@formal@none@1@S@There has been some controversy over which of the alternative formulations of the test Turing intended.@@@@1@16@@oe@26-8-2013
1000009001030@unknown@formal@none@1@S@Sterret argues that two distinct tests can be extracted from Turing's 1950 paper, and that, ⌊/pace/⌋ Turing's remark, they are not equivalent.@@@@1@22@@oe@26-8-2013
1000009001040@unknown@formal@none@1@S@The test that employs the party game and compares frequencies of success in the game is referred to as the "Original Imitation Game Test" whereas the test consisting of a human judge conversing with a human and a machine is referred to as the "Standard Turing Test", noting that Sterret equates this with the "standard interpretation" rather than the second version of the imitation game.@@@@1@65@@oe@26-8-2013
1000009001050@unknown@formal@none@1@S@Sterrett agrees that the Standard Turing Test (STT) has the problems its critics cite, but argues that, in contrast, the Original Imitation Game Test (OIG Test) so defined is immune to many of them, due to a crucial difference: the OIG Test, unlike the STT, does not make similarity to a human performance the criterion of the test, even though it employs a human performance in setting a criterion for machine intelligence.@@@@1@72@@oe@26-8-2013
1000009001060@unknown@formal@none@1@S@A man can fail the OIG Test, but it is argued that this is a virtue of a test of intelligence if failure indicates a lack of resourcefulness.@@@@1@28@@oe@26-8-2013
1000009001070@unknown@formal@none@1@S@It is argued that the OIG Test requires the resourcefulness associated with intelligence and not merely "simulation of human conversational behaviour".@@@@1@21@@oe@26-8-2013
1000009001080@unknown@formal@none@1@S@The general structure of the OIG Test could even be used with nonverbal versions of imitation games.@@@@1@17@@oe@26-8-2013
1000009001090@unknown@formal@none@1@S@Still other writers have interpreted Turing to be proposing that the imitation game itself is the test, without specifying how to take into account Turing's statement that the test he proposed using the party version of the imitation game is based upon a criterion of comparative frequency of success in that imitation game, rather than a capacity to succeed at one round of the game.@@@@1@65@@oe@26-8-2013
1000009001100@unknown@formal@none@1@S@⌊=Should the interrogator know about the computer?¦3=⌋@@@@1@7@@oe@26-8-2013
1000009001110@unknown@formal@none@1@S@Turing never makes it clear as to whether or not the interrogator in his tests is aware that one of the participants is a computer.@@@@1@25@@oe@26-8-2013
1000009001120@unknown@formal@none@1@S@To return to the Original Imitation Game, Turing states only that Player A is to be replaced with a machine, not that player C is to be made aware of this replacement.@@@@1@32@@oe@26-8-2013
1000009001130@unknown@formal@none@1@S@When Colby, Hilf, Weber and Kramer tested PARRY, they did so by assuming that the interrogators did not need to know that one or more of those being interviewed was a computer during the interrogation.@@@@1@35@@oe@26-8-2013
1000009001140@unknown@formal@none@1@S@But, as Saygin and others highlight, this makes a big difference to the implementation and outcome of the test.@@@@1@19@@oe@26-8-2013
1000009001150@unknown@formal@none@1@S@⌊=Strengths of the test¦2=⌋@@@@1@4@@oe@26-8-2013
1000009001160@unknown@formal@none@1@S@The power of the Turing test derives from the fact that it is possible to talk about anything.@@@@1@18@@oe@26-8-2013
1000009001170@unknown@formal@none@1@S@Turing wrote "the question and answer method seems to be suitable for introducing almost any one of the fields of human endeavor that we wish to include."@@@@1@27@@oe@26-8-2013
1000009001180@unknown@formal@none@1@S@⌊>John Haugeland>⌋ adds that "understanding the words is not enough; you have to understand the ⌊/topic/⌋ as well."@@@@1@18@@oe@26-8-2013
1000009001190@unknown@formal@none@1@S@In order to pass a well designed Turing test, the machine would have to use ⌊>natural language>⌋, to ⌊>reason>⌋, to have ⌊>knowledge>⌋ and to ⌊>learn>⌋.@@@@1@25@@oe@26-8-2013
1000009001200@unknown@formal@none@1@S@The test can be extended to include video input, as well as a "hatch" through which objects can be passed, and this would force the machine to demonstrate the skill of ⌊>vision>⌋ and ⌊>robotics>⌋ as well.@@@@1@36@@oe@26-8-2013
1000009001210@unknown@formal@none@1@S@Together these represent almost all the major problems of ⌊>artificial intelligence>⌋.@@@@1@11@@oe@26-8-2013
1000009001220@unknown@formal@none@1@S@⌊=Weaknesses of the test¦2=⌋@@@@1@4@@oe@26-8-2013
1000009001230@unknown@formal@none@1@S@The test has been criticized on several grounds.@@@@1@8@@oe@26-8-2013
1000009001240@unknown@formal@none@1@S@⌊=Human intelligence vs. intelligence in general¦3=⌋@@@@1@6@@oe@26-8-2013
1000009001250@unknown@formal@none@1@S@The test is explicitly ⌊>anthropomorphic>⌋.@@@@1@5@@oe@26-8-2013
1000009001260@unknown@formal@none@1@S@It only tests if the subject ⌊/resembles/⌋ a human being.@@@@1@10@@oe@26-8-2013
1000009001270@unknown@formal@none@1@S@It will fail to test for intelligence under two circumstances:@@@@1@10@@oe@26-8-2013
1000009001280@unknown@formal@none@1@S@⌊•⌊#It tests for many behaviors that we may not consider intelligent, such as the susceptibility to insults or the temptation to lie.@@@@1@22@@oe@26-8-2013
1000009001290@unknown@formal@none@1@S@A machine may very well be intelligent without being able to chat ⌊/exactly/⌋ like a human.#⌋@@@@1@16@@oe@26-8-2013
1000009001300@unknown@formal@none@1@S@⌊#It fails to capture the ⌊/general/⌋ properties of intelligence, such as the ability to solve difficult problems or come up with original insights.@@@@1@23@@oe@26-8-2013
1000009001310@unknown@formal@none@1@S@If a machine can solve a difficult problem that no person could solve, it would, in principle, fail the test.#⌋•⌋@@@@1@20@@oe@26-8-2013
1000009001320@unknown@formal@none@1@S@⌊>Stuart J. Russell>⌋ and ⌊>Peter Norvig>⌋ argue that the anthropomorphism of the test prevents it from being truly useful for the task of engineering intelligent machines.@@@@1@26@@oe@26-8-2013
1000009001330@unknown@formal@none@1@S@They write: "Aeronautical engineering texts do not define the goal of their field as 'making machines that fly so exactly like pigeons that they can fool other pigeons.'"@@@@1@28@@oe@26-8-2013
1000009001340@unknown@formal@none@1@S@The test is also vulnerable to naivete on the part of the test subjects.@@@@1@14@@oe@26-8-2013
1000009001350@unknown@formal@none@1@S@If the testers have little experience with ⌊>chatterbot>⌋s they may be more likely to judge a computer program to be responding coherently than someone who is aware of the various tricks that chatterbots use, such as changing the subject or answering a question with another question.@@@@1@46@@oe@26-8-2013
1000009001360@unknown@formal@none@1@S@Such tricks may be misinterpreted as "playfulness" and therefore evidence of a human participant by uninformed testers, especially during brief sessions in which a chatterbot's inherent repetitiveness does not have a chance to become evident.@@@@1@35@@oe@26-8-2013
1000009001370@unknown@formal@none@1@S@⌊=Real intelligence vs. simulated intelligence¦3=⌋@@@@1@5@@oe@26-8-2013
1000009001380@unknown@formal@none@1@S@The test is also explicitly ⌊>behaviorist>⌋ or ⌊>functionalist>⌋: it only tests how the subject ⌊/acts./⌋@@@@1@15@@oe@26-8-2013
1000009001390@unknown@formal@none@1@S@A machine passing the Turing test may be able to ⌊/simulate human conversational behaviour/⌋ but the machine might just follow some cleverly devised rules.@@@@1@24@@oe@26-8-2013
1000009001400@unknown@formal@none@1@S@Two famous examples of this line of argument against the Turing test are ⌊>John Searle>⌋'s ⌊>Chinese room>⌋ argument and ⌊>Ned Block>⌋'s ⌊>Blockhead>⌋ argument.@@@@1@23@@oe@26-8-2013
1000009001410@unknown@formal@none@1@S@Even if the Turing test is a good operational definition of intelligence, it may not indicate that the machine has ⌊>consciousness>⌋, or that it has ⌊>intentionality>⌋.@@@@1@26@@oe@26-8-2013
1000009001420@unknown@formal@none@1@S@Perhaps intelligence and consciousness, for example, are such that neither one necessarily implies the other.@@@@1@15@@oe@26-8-2013
1000009001430@unknown@formal@none@1@S@In that case, the Turing test might fail to capture one of the key differences between intelligent machines and intelligent people.@@@@1@21@@oe@26-8-2013
1000009001440@unknown@formal@none@1@S@⌊=Predictions and tests¦2=⌋@@@@1@3@@oe@26-8-2013
1000009001450@unknown@formal@none@1@S@Turing predicted that machines would eventually be able to pass the test.@@@@1@12@@oe@26-8-2013
1000009001460@unknown@formal@none@1@S@In fact, he estimated that by the year 2000, machines with 10⌊^9^⌋ ⌊>bit>⌋s (about 119.2 ⌊>MiB>⌋) of memory would be able to fool 30% of human judges during a 5-minute test.@@@@1@31@@oe@26-8-2013
1000009001470@unknown@formal@none@1@S@He also predicted that people would then no longer consider the phrase "thinking machine" contradictory.@@@@1@15@@oe@26-8-2013
1000009001480@unknown@formal@none@1@S@He further predicted that ⌊>machine learning>⌋ would be an important part of building powerful machines, a claim which is considered to be plausible by contemporary researchers in ⌊>Artificial intelligence>⌋.@@@@1@29@@oe@26-8-2013
1000009001490@unknown@formal@none@1@S@By extrapolating an ⌊>exponential growth>⌋ of technology over several decades, ⌊>futurist>⌋ ⌊>Ray Kurzweil>⌋ predicted that Turing-test-capable computers would be manufactured around the year 2020, roughly speaking.@@@@1@26@@oe@26-8-2013
1000009001500@unknown@formal@none@1@S@See the ⌊>Moore's Law>⌋ article and the references therein for discussions of the plausibility of this argument.@@@@1@17@@oe@26-8-2013
1000009001510@unknown@formal@none@1@S@⌊>As of 2008>⌋, no computer has passed the Turing test as such.@@@@1@12@@oe@26-8-2013
1000009001520@unknown@formal@none@1@S@Simple conversational programs such as ⌊>ELIZA>⌋ have fooled people into believing they are talking to another human being, such as in an informal experiment termed ⌊>AOLiza>⌋.@@@@1@26@@oe@26-8-2013
1000009001530@unknown@formal@none@1@S@However, such "successes" are not the same as a Turing Test.@@@@1@11@@oe@26-8-2013
1000009001540@unknown@formal@none@1@S@Most obviously, the human party in the conversation has no reason to suspect they are talking to anything other than a human, whereas in a real Turing test the questioner is actively trying to determine the nature of the entity they are chatting with.@@@@1@44@@oe@26-8-2013
1000009001550@unknown@formal@none@1@S@Documented cases are usually in environments such as ⌊>Internet Relay Chat>⌋ where conversation is sometimes stilted and meaningless, and in which no understanding of a conversation is necessary.@@@@1@28@@oe@26-8-2013
1000009001560@unknown@formal@none@1@S@Additionally, many internet relay chat participants use English as a second or third language, thus making it even more likely that they would assume that an unintelligent comment by the conversational program is simply something they have misunderstood, and do not recognize the very non-human errors they make.@@@@1@48@@oe@26-8-2013
1000009001570@unknown@formal@none@1@S@See ⌊>ELIZA effect>⌋.@@@@1@3@@oe@26-8-2013
1000009001580@unknown@formal@none@1@S@The ⌊>Loebner prize>⌋ is an annual competition to determine the best Turing test competitors.@@@@1@14@@oe@26-8-2013
1000009001590@unknown@formal@none@1@S@Although they award an annual prize for the computer system that, in the judges' opinions, demonstrates the "most human" conversational behaviour (with learning AI ⌊>Jabberwacky>⌋ winning in ⌊>2005>⌋ and ⌊>2006>⌋, and ⌊>A.L.I.C.E.>⌋ before that), they have an additional prize for a system that in their opinion passes a Turing test.@@@@1@50@@oe@26-8-2013
1000009001600@unknown@formal@none@1@S@This second prize has not yet been awarded.@@@@1@8@@oe@26-8-2013
1000009001610@unknown@formal@none@1@S@The creators of Jabberwacky have proposed a personal Turing Test: the ability to pass the imitation test while attempting to specifically imitate the human player, with whom the AI will have conversed at length before the test.@@@@1@37@@oe@26-8-2013
1000009001620@unknown@formal@none@1@S@In ⌊>2008>⌋ the competition for the ⌊>Loebner prize>⌋ is being co-organised by ⌊>Kevin Warwick>⌋ and held at the ⌊>University of Reading>⌋ on ⌊>October 12>⌋.@@@@1@24@@oe@26-8-2013
1000009001630@unknown@formal@none@1@S@The directive for the competition is to stay as close as possible to Turing's original statements made in his 1950 paper, such that it can be ascertained if any machines are presently close to 'passing the test'.@@@@1@37@@oe@26-8-2013
1000009001640@unknown@formal@none@1@S@An academic meeting discussing the Turing Test, organised by the ⌊>Society for the Study of Artificial Intelligence and the Simulation of Behaviour>⌋, is being held in parallel at the same venue.@@@@1@31@@oe@26-8-2013
1000009001650@unknown@formal@none@1@S@Trying to pass the Turing test in its full generality is not, as of 2005, an active focus of much mainstream academic or commercial effort.@@@@1@25@@oe@26-8-2013
1000009001660@unknown@formal@none@1@S@Current research in AI-related fields is aimed at more modest and specific goals.@@@@1@13@@oe@26-8-2013
1000009001670@unknown@formal@none@1@S@The first bet of the ⌊>Long Bet Project>⌋ is a ⌊>$>⌋10,000 one between ⌊>Mitch Kapor>⌋ (pessimist) and ⌊>Ray Kurzweil>⌋ (optimist) about whether a computer will pass a Turing Test by the year ⌊>2029>⌋.@@@@1@33@@oe@26-8-2013
1000009001680@unknown@formal@none@1@S@The bet specifies the conditions in some detail.@@@@1@8@@oe@26-8-2013
1000009001690@unknown@formal@none@1@S@⌊=Variations of the Turing test¦2=⌋@@@@1@5@@oe@26-8-2013
1000009001700@unknown@formal@none@1@S@A modification of the Turing test, where the objective or one or more of the roles have been reversed between computers and humans, is termed a ⌊>reverse Turing test>⌋.@@@@1@29@@oe@26-8-2013
1000009001710@unknown@formal@none@1@S@Another variation of the Turing test is described as the ⌊>Subject matter expert Turing test>⌋ where a computer's response cannot be distinguished from an expert in a given field.@@@@1@29@@oe@26-8-2013
1000009001720@unknown@formal@none@1@S@As brain and body scanning techniques improve it may also be possible to replicate the essential ⌊>data element>⌋s of a person to a computer system.@@@@1@25@@oe@26-8-2013
1000009001730@unknown@formal@none@1@S@The ⌊>Immortality test>⌋ variation of the Turing test would determine if a person's essential character is reproduced with enough fidelity to make it impossible to distinguish a reproduction of a person from the original person.@@@@1@35@@oe@26-8-2013
1000009001740@unknown@formal@none@1@S@The ⌊>Minimum Intelligent Signal Test>⌋ proposed by ⌊>Chris McKinstry>⌋, is another variation of Turing's test, but where only binary responses are permitted.@@@@1@22@@oe@26-8-2013
1000009001750@unknown@formal@none@1@S@It is typically used to gather statistical data against which the performance of ⌊>artificial intelligence>⌋ programs may be measured.@@@@1@19@@oe@26-8-2013
1000009001760@unknown@formal@none@1@S@Another variation of the reverse Turing test is implied in the work of psychoanalyst Wilfred Bion, who was particularly fascinated by the "storm" that resulted from the encounter of one mind by another.@@@@1@33@@oe@26-8-2013
1000009001770@unknown@formal@none@1@S@Carrying this idea forward, R. D. Hinshelwood described the mind as a "mind recognizing apparatus", noting that this might be some sort of "supplement" to the Turing test.@@@@1@28@@oe@26-8-2013
1000009001780@unknown@formal@none@1@S@To make this more explicit, the challenge would be for the computer to be able to determine if it were interacting with a human or another computer.@@@@1@27@@oe@26-8-2013
1000009001790@unknown@formal@none@1@S@This is an extension of the original question Turing was attempting to answer, but would, perhaps, be a high enough standard to define a machine that could "think" in a way we typically define as characteristically human.@@@@1@37@@oe@26-8-2013
1000009001800@unknown@formal@none@1@S@Another variation is the Meta Turing test, in which the subject being tested (for example a computer) is classified as intelligent if it itself has created something that the subject itself wants to test for intelligence.@@@@1@36@@oe@26-8-2013
1000009001810@unknown@formal@none@1@S@⌊=Practical applications¦2=⌋@@@@1@2@@oe@26-8-2013
1000009001820@unknown@formal@none@1@S@⌊>Stuart J. Russell>⌋ and ⌊>Peter Norvig>⌋ note that "AI researchers have devoted little attention to passing the Turing Test",@@@@1@19@@oe@26-8-2013
1000009001830@unknown@formal@none@1@S@Real Turing tests, such as the ⌊>Loebner prize>⌋, do not usually force programs to demonstrate the full range of intelligence and are reserved for testing ⌊>chatterbot>⌋ programs.@@@@1@27@@oe@26-8-2013
1000009001840@unknown@formal@none@1@S@However, even in this limited form these tests are still very rigorous.@@@@1@12@@oe@26-8-2013
1000009001850@unknown@formal@none@1@S@The 2008 ⌊>Loebner prize>⌋ however is sticking closely to Turing's original concepts - for example conversations will be for 5 minutes only.@@@@1@22@@oe@26-8-2013
1000009001860@unknown@formal@none@1@S@⌊>CAPTCHA>⌋ is a form of ⌊>reverse Turing test>⌋.@@@@1@8@@oe@26-8-2013
1000009001870@unknown@formal@none@1@S@Before being allowed to do some action on a ⌊>website>⌋, the user is presented with alphanumerical characters in a distorted graphic image and asked to recognise it.@@@@1@27@@oe@26-8-2013
1000009001880@unknown@formal@none@1@S@This is intended to prevent automated systems from abusing the site.@@@@1@11@@oe@26-8-2013
1000009001890@unknown@formal@none@1@S@The rationale is that software sufficiently sophisticated to read the distorted image accurately does not exist (or is not available to the average user), so any system able to do so is likely to be a human being.@@@@1@38@@oe@26-8-2013
1000009001900@unknown@formal@none@1@S@⌊=In popular culture¦2=⌋@@@@1@3@@oe@26-8-2013
1000009001910@unknown@formal@none@1@S@In the ⌊/⌊>Dilbert>⌋/⌋ comic strip on Sunday ⌊>30 March>⌋ ⌊>2008>⌋,, Dilbert says, "The security audit accidentally locked all of the developers out of the system", and his boss responds with only meaningless, ⌊>tautological>⌋ ⌊>thought-terminating cliché>⌋s, "Well, it is what it is."@@@@1@41@@oe@26-8-2013
1000009001911@unknown@formal@none@1@S@Dilbert asks "How does that help" and his boss responds with another cliche, "You don't know what you don't know."@@@@1@20@@oe@26-8-2013
1000009001920@unknown@formal@none@1@S@Dilbert replies, "Congratulations.@@@@1@3@@oe@26-8-2013
1000009001930@unknown@formal@none@1@S@You're the first human to fail the Turing Test."@@@@1@9@@oe@26-8-2013
1000009001940@unknown@formal@none@1@S@For that day, "turing test" was the 43⌊^rd^⌋ most popular ⌊>Google>⌋ search.@@@@1@12@@oe@26-8-2013
1000009001950@unknown@formal@none@1@S@The character of ⌊>Ghostwheel>⌋ in ⌊>Roger Zelazny>⌋'s ⌊>The Chronicles of Amber>⌋ is mentioned to be capable of passing the Turing Test.@@@@1@21@@oe@26-8-2013
1000009001960@unknown@formal@none@1@S@The webcomic ⌊>xkcd>⌋ has referred to Turing and the Turing test.@@@@1@11@@oe@26-8-2013
1000009001970@unknown@formal@none@1@S@⌊>Rick Deckard>⌋,in the movie ⌊>Blade Runner>⌋, used a Turing Test to determine if Rachael was a ⌊>Replicant>⌋.@@@@1@17@@oe@26-8-2013