1000007200010@unknown@formal@none@1@S@⌊δPredictive analyticsδ⌋@@@@1@2@@oe@26-8-2013
1000007200020@unknown@formal@none@1@S@⌊∗Predictive analytics∗⌋ encompasses a variety of techniques from ⌊>statistics>⌋ and ⌊>data mining>⌋ that analyze current and historical data to make predictions about future events.@@@@1@24@@oe@26-8-2013
1000007200030@unknown@formal@none@1@S@Such predictions rarely take the form of absolute statements, and are more likely to be expressed as values that correspond to the odds of a particular event or behavior taking place in the future.@@@@1@34@@oe@26-8-2013
1000007200040@unknown@formal@none@1@S@In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities.@@@@1@17@@oe@26-8-2013
1000007200050@unknown@formal@none@1@S@Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.@@@@1@26@@oe@26-8-2013
1000007200060@unknown@formal@none@1@S@One of the most well-known applications is ⌊>credit scoring>⌋, which is used throughout ⌊>financial services>⌋.@@@@1@15@@oe@26-8-2013
1000007200070@unknown@formal@none@1@S@Scoring models process a customer’s ⌊>credit history>⌋, ⌊>loan application>⌋, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.@@@@1@27@@oe@26-8-2013
1000007200080@unknown@formal@none@1@S@Predictive analytics are also used in ⌊>insurance>⌋, ⌊>telecommunications>⌋, ⌊>retail>⌋, ⌊>travel>⌋, ⌊>healthcare>⌋, ⌊>pharmaceuticals>⌋ and other fields.@@@@1@15@@oe@26-8-2013
1000007200090@unknown@formal@none@1@S@⌊=Types of predictive analytics¦2=⌋@@@@1@4@@oe@26-8-2013
1000007200100@unknown@formal@none@1@S@Generally, predictive analytics is used to mean ⌊>predictive modeling>⌋, scoring of predictive models, and ⌊>forecasting>⌋.@@@@1@15@@oe@26-8-2013
1000007200110@unknown@formal@none@1@S@However, people are increasingly using the term to describe related analytic disciplines, such as descriptive modeling and decision modeling or optimization.@@@@1@21@@oe@26-8-2013
1000007200120@unknown@formal@none@1@S@These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but have different purposes and the statistical techniques underlying them vary.@@@@1@29@@oe@26-8-2013
1000007200130@unknown@formal@none@1@S@⌊=Predictive models¦3=⌋@@@@1@2@@oe@26-8-2013
1000007200140@unknown@formal@none@1@S@Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve ⌊>marketing effectiveness>⌋.@@@@1@26@@oe@26-8-2013
1000007200150@unknown@formal@none@1@S@This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models.@@@@1@22@@oe@26-8-2013
1000007200160@unknown@formal@none@1@S@Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision.@@@@1@28@@oe@26-8-2013
1000007200170@unknown@formal@none@1@S@⌊=Descriptive models¦3=⌋@@@@1@2@@oe@26-8-2013
1000007200180@unknown@formal@none@1@S@Descriptive models “describe” relationships in data in a way that is often used to classify customers or prospects into groups.@@@@1@20@@oe@26-8-2013
1000007200190@unknown@formal@none@1@S@Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products.@@@@1@25@@oe@26-8-2013
1000007200200@unknown@formal@none@1@S@But the descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do.@@@@1@21@@oe@26-8-2013
1000007200210@unknown@formal@none@1@S@Descriptive models are often used “offline,” for example, to categorize customers by their product preferences and life stage.@@@@1@18@@oe@26-8-2013
1000007200220@unknown@formal@none@1@S@Descriptive modeling tools can be utilized to develop agent based models that can simulate large number of individualized agents to predict possible futures.@@@@1@23@@oe@26-8-2013
1000007200230@unknown@formal@none@1@S@⌊=Decision models¦3=⌋@@@@1@2@@oe@26-8-2013
1000007200240@unknown@formal@none@1@S@Decision models describe the relationship between all the elements of a decision — the known data (including results of predictive models), the decision and the forecast results of the decision — in order to predict the results of decisions involving many variables.@@@@1@42@@oe@26-8-2013
1000007200250@unknown@formal@none@1@S@These models can be used in optimization, a data-driven approach to improving decision logic that involves maximizing certain outcomes while minimizing others.@@@@1@22@@oe@26-8-2013
1000007200260@unknown@formal@none@1@S@Decision models are generally used offline, to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.@@@@1@27@@oe@26-8-2013
1000007200270@unknown@formal@none@1@S@⌊=Predictive analytics¦2=⌋@@@@1@2@@oe@26-8-2013
1000007200280@unknown@formal@none@1@S@⌊=Definition¦3=⌋@@@@1@1@@oe@26-8-2013
1000007200290@unknown@formal@none@1@S@Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns.@@@@1@25@@oe@26-8-2013
1000007200300@unknown@formal@none@1@S@The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.@@@@1@26@@oe@26-8-2013
1000007200310@unknown@formal@none@1@S@⌊=Current uses¦3=⌋@@@@1@2@@oe@26-8-2013
1000007200320@unknown@formal@none@1@S@Although predictive analytics can be put to use in many applications, we outline a few examples where predictive analytics has shown positive impact in recent years.@@@@1@26@@oe@26-8-2013
1000007200330@unknown@formal@none@1@S@⌊=Analytical Customer Relationship Management (CRM)¦4=⌋@@@@1@5@@oe@26-8-2013
1000007200340@unknown@formal@none@1@S@Analytical ⌊>Customer Relationship Management>⌋ is a frequent commercial application of Predictive Analysis.@@@@1@12@@oe@26-8-2013
1000007200350@unknown@formal@none@1@S@Methods of predictive analysis are applied to customer data to pursue CRM objectives.@@@@1@13@@oe@26-8-2013
1000007200360@unknown@formal@none@1@S@⌊=Direct marketing¦4=⌋@@@@1@2@@oe@26-8-2013
1000007200370@unknown@formal@none@1@S@Product ⌊>marketing>⌋ is constantly faced with the challenge of coping with the increasing number of competing products, different consumer preferences and the variety of methods (channels) available to interact with each consumer.@@@@1@32@@oe@26-8-2013
1000007200380@unknown@formal@none@1@S@Efficient marketing is a process of understanding the amount of variability and tailoring the marketing strategy for greater profitability.@@@@1@19@@oe@26-8-2013
1000007200390@unknown@formal@none@1@S@Predictive analytics can help identify consumers with a higher likelihood of responding to a particular marketing offer.@@@@1@17@@oe@26-8-2013
1000007200400@unknown@formal@none@1@S@Models can be built using data from consumers’ past purchasing history and past response rates for each channel.@@@@1@18@@oe@26-8-2013
1000007200410@unknown@formal@none@1@S@Additional information about the consumers demographic, geographic and other characteristics can be used to make more accurate predictions.@@@@1@18@@oe@26-8-2013
1000007200420@unknown@formal@none@1@S@Targeting only these consumers can lead to substantial increase in response rate which can lead to a significant reduction in cost per acquisition.@@@@1@23@@oe@26-8-2013
1000007200430@unknown@formal@none@1@S@Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of products and marketing channels that should be used to target a given consumer.@@@@1@29@@oe@26-8-2013
1000007200440@unknown@formal@none@1@S@⌊=Cross-sell¦4=⌋@@@@1@1@@oe@26-8-2013
1000007200450@unknown@formal@none@1@S@Often corporate organizations collect and maintain abundant data (e.g. customer records, sale transactions) and exploiting hidden relationships in the data can provide a competitive advantage to the organization.@@@@1@28@@oe@26-8-2013
1000007200460@unknown@formal@none@1@S@For an organization that offers multiple products, an analysis of existing customer behavior can lead to efficient ⌊>cross sell>⌋ of products.@@@@1@21@@oe@26-8-2013
1000007200470@unknown@formal@none@1@S@This directly leads to higher profitability per customer and strengthening of the customer relationship.@@@@1@14@@oe@26-8-2013
1000007200480@unknown@formal@none@1@S@Predictive analytics can help analyze customers’ spending, usage and other behavior, and help cross-sell the right product at the right time.@@@@1@21@@oe@26-8-2013
1000007200490@unknown@formal@none@1@S@⌊=Customer retention¦4=⌋@@@@1@2@@oe@26-8-2013
1000007200500@unknown@formal@none@1@S@With the amount of competing services available, businesses need to focus efforts on maintaining continuous ⌊>consumer satisfaction>⌋.@@@@1@17@@oe@26-8-2013
1000007200510@unknown@formal@none@1@S@In such a competitive scenario, ⌊>consumer loyalty>⌋ needs to be rewarded and ⌊>customer attrition>⌋ needs to be minimized.@@@@1@18@@oe@26-8-2013
1000007200520@unknown@formal@none@1@S@Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service.@@@@1@23@@oe@26-8-2013
1000007200530@unknown@formal@none@1@S@At this stage, the chance of changing the customer’s decision is almost impossible.@@@@1@13@@oe@26-8-2013
1000007200540@unknown@formal@none@1@S@Proper application of predictive analytics can lead to a more proactive retention strategy.@@@@1@13@@oe@26-8-2013
1000007200550@unknown@formal@none@1@S@By a frequent examination of a customer’s past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer wanting to terminate service sometime in the near future.@@@@1@35@@oe@26-8-2013
1000007200560@unknown@formal@none@1@S@An intervention with lucrative offers can increase the chance of retaining the customer.@@@@1@13@@oe@26-8-2013
1000007200570@unknown@formal@none@1@S@Silent attrition is the behavior of a customer to slowly but steadily reduce usage and is another problem faced by many companies.@@@@1@22@@oe@26-8-2013
1000007200580@unknown@formal@none@1@S@Predictive analytics can also predict this behavior accurately and before it occurs, so that the company can take proper actions to increase customer activity.@@@@1@24@@oe@26-8-2013
1000007200590@unknown@formal@none@1@S@⌊=Underwriting¦4=⌋@@@@1@1@@oe@26-8-2013
1000007200600@unknown@formal@none@1@S@Many businesses have to account for risk exposure due to their different services and determine the cost needed to cover the risk.@@@@1@22@@oe@26-8-2013
1000007200610@unknown@formal@none@1@S@For example, auto insurance providers need to accurately determine the amount of premium to charge to cover each automobile and driver.@@@@1@21@@oe@26-8-2013
1000007200620@unknown@formal@none@1@S@A financial company needs to assess a borrower’s potential and ability to pay before granting a loan.@@@@1@17@@oe@26-8-2013
1000007200630@unknown@formal@none@1@S@For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future.@@@@1@40@@oe@26-8-2013
1000007200640@unknown@formal@none@1@S@Predictive analytics can help ⌊>underwriting>⌋ of these quantities by predicting the chances of illness, ⌊>default>⌋, ⌊>bankruptcy>⌋, etc.@@@@1@17@@oe@26-8-2013
1000007200650@unknown@formal@none@1@S@Predictive analytics can streamline the process of customer acquisition, by predicting the future risk behavior of a customer using application level data.@@@@1@22@@oe@26-8-2013
1000007200660@unknown@formal@none@1@S@Proper predictive analytics can lead to proper pricing decisions, which can help mitigate future risk of default.@@@@1@17@@oe@26-8-2013
1000007200670@unknown@formal@none@1@S@⌊=Collection analytics¦4=⌋@@@@1@2@@oe@26-8-2013
1000007200680@unknown@formal@none@1@S@Every portfolio has a set of delinquent customers who do not make their payments on time.@@@@1@16@@oe@26-8-2013
1000007200690@unknown@formal@none@1@S@The financial institution has to undertake collection activities on these customers to recover the amounts due.@@@@1@16@@oe@26-8-2013
1000007200700@unknown@formal@none@1@S@A lot of collection resources are wasted on customers who are difficult or impossible to recover.@@@@1@16@@oe@26-8-2013
1000007200710@unknown@formal@none@1@S@Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs.@@@@1@38@@oe@26-8-2013
1000007200720@unknown@formal@none@1@S@⌊=Fraud detection¦4=⌋@@@@1@2@@oe@26-8-2013
1000007200730@unknown@formal@none@1@S@Fraud is a big problem for many businesses and can be of various types.@@@@1@14@@oe@26-8-2013
1000007200740@unknown@formal@none@1@S@Inaccurate credit applications, fraudulent transactions, ⌊>identity theft>⌋s and false insurance claims are some examples of this problem.@@@@1@17@@oe@26-8-2013
1000007200750@unknown@formal@none@1@S@These problems plague firms all across the spectrum and some examples of likely victims are ⌊>credit card issuers>⌋, insurance companies, retail merchants, manufacturers, business to business suppliers and even services providers.@@@@1@31@@oe@26-8-2013
1000007200760@unknown@formal@none@1@S@This is an area where a predictive model is often used to help weed out the “bads” and reduce a business's exposure to fraud.@@@@1@24@@oe@26-8-2013
1000007200770@unknown@formal@none@1@S@⌊=Portfolio, product or economy level prediction¦4=⌋@@@@1@6@@oe@26-8-2013
1000007200780@unknown@formal@none@1@S@Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy.@@@@1@19@@oe@26-8-2013
1000007200790@unknown@formal@none@1@S@For example a retailer might be interested in predicting store level demand for inventory management purposes.@@@@1@16@@oe@26-8-2013
1000007200800@unknown@formal@none@1@S@Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year.@@@@1@17@@oe@26-8-2013
1000007200810@unknown@formal@none@1@S@These type of problems can be addressed by predictive analytics using Time Series techniques (see below).@@@@1@16@@oe@26-8-2013
1000007200820@unknown@formal@none@1@S@Wrong Information....@@@@1@2@@oe@26-8-2013
1000007200830@unknown@formal@none@1@S@⌊=Statistical techniques¦2=⌋@@@@1@2@@oe@26-8-2013
1000007200840@unknown@formal@none@1@S@The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques.@@@@1@20@@oe@26-8-2013
1000007200850@unknown@formal@none@1@S@⌊=Regression Techniques¦4=⌋@@@@1@2@@oe@26-8-2013
1000007200860@unknown@formal@none@1@S@Regression models are the mainstay of predictive analytics.@@@@1@8@@oe@26-8-2013
1000007200870@unknown@formal@none@1@S@The focus lies on establishing a mathematical equation as a model to represent the interactions between the different variables in consideration.@@@@1@21@@oe@26-8-2013
1000007200880@unknown@formal@none@1@S@Depending on the situation, there is a wide variety of models that can be applied while performing predictive analytics.@@@@1@19@@oe@26-8-2013
1000007200890@unknown@formal@none@1@S@Some of them are briefly discussed below.@@@@1@7@@oe@26-8-2013
1000007200900@unknown@formal@none@1@S@⌊=Linear Regression Model¦5=⌋@@@@1@3@@oe@26-8-2013
1000007200910@unknown@formal@none@1@S@The linear regression model analyzes the relationship between the response or dependent variable and a set of independent or predictor variables.@@@@1@21@@oe@26-8-2013
1000007200920@unknown@formal@none@1@S@This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters.@@@@1@19@@oe@26-8-2013
1000007200930@unknown@formal@none@1@S@These parameters are adjusted so that a measure of fit is optimized.@@@@1@12@@oe@26-8-2013
1000007200940@unknown@formal@none@1@S@Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions.@@@@1@31@@oe@26-8-2013
1000007200950@unknown@formal@none@1@S@The goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals.@@@@1@22@@oe@26-8-2013
1000007200960@unknown@formal@none@1@S@This is referred to as ⌊∗⌊>ordinary least squares>⌋∗⌋ (OLS) estimation and results in best linear unbiased estimates (BLUE) of the parameters if and only if the ⌊>Gauss-Markowitz>⌋ assumptions are satisfied.@@@@1@30@@oe@26-8-2013
1000007200970@unknown@formal@none@1@S@Once the model has been estimated we would be interested to know if the predictor variables belong in the model – i.e. is the estimate of each variable’s contribution reliable?@@@@1@30@@oe@26-8-2013
1000007200980@unknown@formal@none@1@S@To do this we can check the statistical significance of the model’s coefficients which can be measured using the t-statistic.@@@@1@20@@oe@26-8-2013
1000007200990@unknown@formal@none@1@S@This amounts to testing whether the coefficient is significantly different from zero.@@@@1@12@@oe@26-8-2013
1000007201000@unknown@formal@none@1@S@How well the model predicts the dependent variable based on the value of the independent variables can be assessed by using the R² statistic.@@@@1@24@@oe@26-8-2013
1000007201010@unknown@formal@none@1@S@It measures predictive power of the model i.e. the proportion of the total variation in the dependent variable that is “explained” (accounted for) by variation in the independent variables.@@@@1@29@@oe@26-8-2013
1000007201020@unknown@formal@none@1@S@⌊=Discrete choice models¦4=⌋@@@@1@3@@oe@26-8-2013
1000007201030@unknown@formal@none@1@S@Multivariate regression (above) is generally used when the response variable is continuous and has an unbounded range.@@@@1@17@@oe@26-8-2013
1000007201040@unknown@formal@none@1@S@Often the response variable may not be continuous but rather discrete.@@@@1@11@@oe@26-8-2013
1000007201050@unknown@formal@none@1@S@While mathematically it is feasible to apply multivariate regression to discrete ordered dependent variables, some of the assumptions behind the theory of multivariate linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis.@@@@1@47@@oe@26-8-2013
1000007201060@unknown@formal@none@1@S@If the dependent variable is discrete, some of those superior methods are ⌊>logistic regression>⌋, ⌊>multinomial logit>⌋ and ⌊>probit>⌋ models.@@@@1@19@@oe@26-8-2013
1000007201070@unknown@formal@none@1@S@Logistic regression and probit models are used when the dependent variable is ⌊>binary>⌋.@@@@1@13@@oe@26-8-2013
1000007201080@unknown@formal@none@1@S@⌊=Logistic regression¦5=⌋@@@@1@2@@oe@26-8-2013
1000007201090@unknown@formal@none@1@S@In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model (See Allison’s Logistic Regression for more information on the theory of Logistic Regression).@@@@1@56@@oe@26-8-2013
1000007201100@unknown@formal@none@1@S@The ⌊>Wald>⌋ and ⌊>likelihood-ratio test>⌋ are used to test the statistical significance of each coefficient b in the model (analogous to the t tests used in OLS regression; see above).@@@@1@30@@oe@26-8-2013
1000007201110@unknown@formal@none@1@S@A test assessing the goodness-of-fit of a classification model is the ⌊>Hosmer and Lemeshow test>⌋.@@@@1@15@@oe@26-8-2013
1000007201120@unknown@formal@none@1@S@⌊=Multinomial logistic regression¦5=⌋@@@@1@3@@oe@26-8-2013
1000007201130@unknown@formal@none@1@S@An extension of the ⌊>binary logit model>⌋ to cases where the dependent variable has more than 2 categories is the ⌊>multinomial logit model>⌋.@@@@1@23@@oe@26-8-2013
1000007201140@unknown@formal@none@1@S@In such cases collapsing the data into two categories might not make good sense or may lead to loss in the richness of the data.@@@@1@25@@oe@26-8-2013
1000007201150@unknown@formal@none@1@S@The multinomial logit model is the appropriate technique in these cases, especially when the dependent variable categories are not ordered (for examples colors like red, blue, green).@@@@1@27@@oe@26-8-2013
1000007201160@unknown@formal@none@1@S@Some authors have extended multinomial regression to include feature selection/importance methods such as ⌊>Random multinomial logit>⌋.@@@@1@16@@oe@26-8-2013
1000007201170@unknown@formal@none@1@S@⌊=Probit regression¦5=⌋@@@@1@2@@oe@26-8-2013
1000007201180@unknown@formal@none@1@S@Probit models offer an alternative to logistic regression for modeling categorical dependent variables.@@@@1@13@@oe@26-8-2013
1000007201190@unknown@formal@none@1@S@Even though the outcomes tend to be similar, the underlying distributions are different.@@@@1@13@@oe@26-8-2013
1000007201200@unknown@formal@none@1@S@Probit models are popular in social sciences like economics.@@@@1@9@@oe@26-8-2013
1000007201210@unknown@formal@none@1@S@A good way to understand the key difference between probit and logit models, is to assume that there is a latent variable z.@@@@1@23@@oe@26-8-2013
1000007201220@unknown@formal@none@1@S@We do not observe z but instead observe y which takes the value 0 or 1.@@@@1@16@@oe@26-8-2013
1000007201230@unknown@formal@none@1@S@In the logit model we assume that follows a logistic distribution.@@@@1@11@@oe@26-8-2013
1000007201240@unknown@formal@none@1@S@In the probit model we assume that follows a standard normal distribution.@@@@1@12@@oe@26-8-2013
1000007201250@unknown@formal@none@1@S@Note that in social sciences (example economics), probit is often used to model situations where the observed variable y is continuous but takes values between 0 and 1.@@@@1@28@@oe@26-8-2013
1000007201260@unknown@formal@none@1@S@⌊=Logit vs. Probit¦5=⌋@@@@1@3@@oe@26-8-2013
1000007201270@unknown@formal@none@1@S@The Probit model has been around longer than the logit model.@@@@1@11@@oe@26-8-2013
1000007201280@unknown@formal@none@1@S@They look identical, except that the logistic distribution tends to be a little flat tailed.@@@@1@15@@oe@26-8-2013
1000007201290@unknown@formal@none@1@S@In fact one of the reasons the logit model was formulated was that the probit model was extremely hard to compute because it involved calculating difficult integrals.@@@@1@27@@oe@26-8-2013
1000007201300@unknown@formal@none@1@S@Modern computing however has made this computation fairly simple.@@@@1@9@@oe@26-8-2013
1000007201310@unknown@formal@none@1@S@The coefficients obtained from the logit and probit model are also fairly close.@@@@1@13@@oe@26-8-2013
1000007201320@unknown@formal@none@1@S@However the odds ratio makes the logit model easier to interpret.@@@@1@11@@oe@26-8-2013
1000007201330@unknown@formal@none@1@S@For practical purposes the only reasons for choosing the probit model over the logistic model would be:@@@@1@17@@oe@26-8-2013
1000007201340@unknown@formal@none@1@S@⌊•⌊#There is a strong belief that the underlying distribution is normal#⌋@@@@1@11@@oe@26-8-2013
1000007201350@unknown@formal@none@1@S@⌊#The actual event is not a binary outcome (e.g. Bankrupt/not bankrupt) but a proportion (e.g. Proportion of population at different debt levels).#⌋•⌋@@@@1@22@@oe@26-8-2013
1000007201360@unknown@formal@none@1@S@⌊=Time series models¦4=⌋@@@@1@3@@oe@26-8-2013
1000007201370@unknown@formal@none@1@S@⌊>Time series>⌋ models are used for predicting or forecasting the future behavior of variables.@@@@1@14@@oe@26-8-2013
1000007201380@unknown@formal@none@1@S@These models account for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.@@@@1@29@@oe@26-8-2013
1000007201390@unknown@formal@none@1@S@As a result standard regression techniques cannot be applied to time series data and methodology has been developed to decompose the trend, seasonal and cyclical component of the series.@@@@1@29@@oe@26-8-2013
1000007201400@unknown@formal@none@1@S@Modeling the dynamic path of a variable can improve forecasts since the predictable component of the series can be projected into the future.@@@@1@23@@oe@26-8-2013
1000007201410@unknown@formal@none@1@S@Time series models estimate difference equations containing stochastic components.@@@@1@9@@oe@26-8-2013
1000007201420@unknown@formal@none@1@S@Two commonly used forms of these models are ⌊>autoregressive model>⌋s (AR) and ⌊>moving average>⌋ (MA) models.@@@@1@16@@oe@26-8-2013
1000007201430@unknown@formal@none@1@S@The ⌊>Box-Jenkins>⌋ methodology (1976) developed by George Box and G.M. Jenkins combines the AR and MA models to produce the ⌊>ARMA>⌋ (autoregressive moving average) model which is the cornerstone of stationary time series analysis.@@@@1@34@@oe@26-8-2013
1000007201440@unknown@formal@none@1@S@ARIMA (autoregressive integrated moving average models) on the other hand are used to describe non-stationary time series.@@@@1@17@@oe@26-8-2013
1000007201450@unknown@formal@none@1@S@Box and Jenkins suggest differencing a non stationary time series to obtain a stationary series to which an ARMA model can be applied.@@@@1@23@@oe@26-8-2013
1000007201460@unknown@formal@none@1@S@Non stationary time series have a pronounced trend and do not have a constant long-run mean or variance.@@@@1@18@@oe@26-8-2013
1000007201470@unknown@formal@none@1@S@Box and Jenkins proposed a three stage methodology which includes: model identification, estimation and validation.@@@@1@15@@oe@26-8-2013
1000007201480@unknown@formal@none@1@S@The identification stage involves identifying if the series is stationary or not and the presence of seasonality by examining plots of the series, autocorrelation and partial autocorrelation functions.@@@@1@28@@oe@26-8-2013
1000007201490@unknown@formal@none@1@S@In the estimation stage, models are estimated using non-linear time series or maximum likelihood estimation procedures.@@@@1@16@@oe@26-8-2013
1000007201500@unknown@formal@none@1@S@Finally the validation stage involves diagnostic checking such as plotting the residuals to detect outliers and evidence of model fit.@@@@1@20@@oe@26-8-2013
1000007201510@unknown@formal@none@1@S@In recent years time series models have become more sophisticated and attempt to model conditional heteroskedasticity with models such as ARCH (⌊>autoregressive conditional heteroskedasticity>⌋) and GARCH (generalized autoregressive conditional heteroskedasticity) models frequently used for financial time series.@@@@1@37@@oe@26-8-2013
1000007201520@unknown@formal@none@1@S@In addition time series models are also used to understand inter-relationships among economic variables represented by systems of equations using VAR (vector autoregression) and structural VAR models.@@@@1@27@@oe@26-8-2013
1000007201530@unknown@formal@none@1@S@⌊=Survival or duration analysis¦4=⌋@@@@1@4@@oe@26-8-2013
1000007201540@unknown@formal@none@1@S@⌊>Survival analysis>⌋ is another name for time to event analysis.@@@@1@10@@oe@26-8-2013
1000007201550@unknown@formal@none@1@S@These techniques were primarily developed in the medical and biological sciences, but they are also widely used in the social sciences like economics, as well as in engineering (reliability and failure time analysis).@@@@1@33@@oe@26-8-2013
1000007201560@unknown@formal@none@1@S@Censoring and non-normality which are characteristic of survival data generate difficulty when trying to analyze the data using conventional statistical models such as multiple linear regression.@@@@1@26@@oe@26-8-2013
1000007201570@unknown@formal@none@1@S@The Normal distribution, being a symmetric distribution, takes positive as well as negative values, but duration by its very nature cannot be negative and therefore normality cannot be assumed when dealing with duration/survival data.@@@@1@34@@oe@26-8-2013
1000007201580@unknown@formal@none@1@S@Hence the normality assumption of regression models is violated.@@@@1@9@@oe@26-8-2013
1000007201590@unknown@formal@none@1@S@A censored observation is defined as an observation with incomplete information.@@@@1@11@@oe@26-8-2013
1000007201600@unknown@formal@none@1@S@Censoring introduces distortions into traditional statistical methods and is essentially a defect of the sample data.@@@@1@16@@oe@26-8-2013
1000007201610@unknown@formal@none@1@S@The assumption is that if the data were not censored it would be representative of the population of interest.@@@@1@19@@oe@26-8-2013
1000007201620@unknown@formal@none@1@S@In survival analysis, censored observations arise whenever the dependent variable of interest represents the time to a terminal event, and the duration of the study is limited in time.@@@@1@29@@oe@26-8-2013
1000007201630@unknown@formal@none@1@S@An important concept in survival analysis is the hazard rate.@@@@1@10@@oe@26-8-2013
1000007201640@unknown@formal@none@1@S@The hazard rate is defined as the probability that the event will occur at time t conditional on surviving until time t.@@@@1@22@@oe@26-8-2013
1000007201650@unknown@formal@none@1@S@Another concept related to the hazard rate is the survival function which can be defined as the probability of surviving to time t.@@@@1@23@@oe@26-8-2013
1000007201660@unknown@formal@none@1@S@Most models try to model the hazard rate by choosing the underlying distribution depending on the shape of the hazard function.@@@@1@21@@oe@26-8-2013
1000007201670@unknown@formal@none@1@S@A distribution whose hazard function slopes upward is said to have positive duration dependence, a decreasing hazard shows negative duration dependence whereas constant hazard is a process with no memory usually characterized by the exponential distribution.@@@@1@36@@oe@26-8-2013
1000007201680@unknown@formal@none@1@S@Some of the distributional choices in survival models are: F, gamma, Weibull, log normal, inverse normal, exponential etc.@@@@1@18@@oe@26-8-2013
1000007201690@unknown@formal@none@1@S@All these distributions are for a non-negative random variable.@@@@1@9@@oe@26-8-2013
1000007201700@unknown@formal@none@1@S@Duration models can be parametric, non-parametric or semi-parametric.@@@@1@8@@oe@26-8-2013
1000007201710@unknown@formal@none@1@S@Some of the models commonly used are Kaplan-Meier, Cox proportional hazard model (non parametric).@@@@1@14@@oe@26-8-2013
1000007201720@unknown@formal@none@1@S@⌊=Classification and regression trees¦4=⌋@@@@1@4@@oe@26-8-2013
1000007201730@unknown@formal@none@1@S@Classification and regression trees (CART) is a ⌊>non-parametric>⌋ technique that produces either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively.@@@@1@27@@oe@26-8-2013
1000007201740@unknown@formal@none@1@S@Trees are formed by a collection of rules based on values of certain variables in the modeling data set@@@@1@19@@oe@26-8-2013
1000007201750@unknown@formal@none@1@S@⌊•⌊#Rules are selected based on how well splits based on variables’ values can differentiate observations based on the dependent variable#⌋@@@@1@20@@oe@26-8-2013
1000007201760@unknown@formal@none@1@S@⌊#Once a rule is selected and splits a node into two, the same logic is applied to each “child” node (i.e. it is a recursive procedure)#⌋@@@@1@26@@oe@26-8-2013
1000007201770@unknown@formal@none@1@S@⌊#Splitting stops when CART detects no further gain can be made, or some pre-set stopping rules are met#⌋•⌋@@@@1@18@@oe@26-8-2013
1000007201780@unknown@formal@none@1@S@Each branch of the tree ends in a terminal node@@@@1@10@@oe@26-8-2013
1000007201790@unknown@formal@none@1@S@⌊•⌊#Each observation falls into one and exactly one terminal node#⌋@@@@1@10@@oe@26-8-2013
1000007201800@unknown@formal@none@1@S@⌊#Each terminal node is uniquely defined by a set of rules#⌋•⌋@@@@1@11@@oe@26-8-2013
1000007201810@unknown@formal@none@1@S@A very popular method for predictive analytics is Leo Breiman's ⌊>Random forests>⌋ or derived versions of this technique like ⌊>Random multinomial logit>⌋.@@@@1@22@@oe@26-8-2013
1000007201820@unknown@formal@none@1@S@⌊=Multivariate adaptive regression splines¦4=⌋@@@@1@4@@oe@26-8-2013
1000007201830@unknown@formal@none@1@S@⌊>Multivariate adaptive regression splines>⌋ (MARS) is a ⌊>non-parametric>⌋ technique that builds flexible models by fitting ⌊>piecewise linear regression>⌋s.@@@@1@18@@oe@26-8-2013
1000007201840@unknown@formal@none@1@S@An important concept associated with regression splines is that of a knot.@@@@1@12@@oe@26-8-2013
1000007201850@unknown@formal@none@1@S@Knot is where one local regression model gives way to another and thus is the point of intersection between two splines.@@@@1@21@@oe@26-8-2013
1000007201860@unknown@formal@none@1@S@In multivariate and adaptive regression splines, ⌊>basis function>⌋s are the tool used for generalizing the search for knots.@@@@1@18@@oe@26-8-2013
1000007201870@unknown@formal@none@1@S@Basis functions are a set of functions used to represent the information contained in one or more variables.@@@@1@18@@oe@26-8-2013
1000007201880@unknown@formal@none@1@S@Multivariate and Adaptive Regression Splines model almost always creates the basis functions in pairs.@@@@1@14@@oe@26-8-2013
1000007201890@unknown@formal@none@1@S@Multivariate and adaptive regression spline approach deliberately overfits the model and then prunes to get to the optimal model.@@@@1@19@@oe@26-8-2013
1000007201900@unknown@formal@none@1@S@The algorithm is computationally very intensive and in practice we are required to specify an upper limit on the number of basis functions.@@@@1@23@@oe@26-8-2013
1000007201910@unknown@formal@none@1@S@⌊=Machine learning techniques¦3=⌋@@@@1@3@@oe@26-8-2013
1000007201920@unknown@formal@none@1@S@⌊>Machine learning>⌋, a branch of artificial intelligence, was originally employed to develop techniques to enable computers to learn.@@@@1@18@@oe@26-8-2013
1000007201930@unknown@formal@none@1@S@Today, since it includes a number of advanced statistical methods for regression and classification, it finds application in a wide variety of fields including ⌊>medical diagnostics>⌋, ⌊>credit card fraud detection>⌋, ⌊>face>⌋ and ⌊>speech recognition>⌋ and analysis of the ⌊>stock market>⌋.@@@@1@40@@oe@26-8-2013
1000007201940@unknown@formal@none@1@S@In certain applications it is sufficient to directly predict the dependent variable without focusing on the underlying relationships between variables.@@@@1@20@@oe@26-8-2013
1000007201950@unknown@formal@none@1@S@In other cases, the underlying relationships can be very complex and the mathematical form of the dependencies unknown.@@@@1@18@@oe@26-8-2013
1000007201960@unknown@formal@none@1@S@For such cases, machine learning techniques emulate ⌊>human cognition>⌋ and learn from training examples to predict future events.@@@@1@18@@oe@26-8-2013
1000007201970@unknown@formal@none@1@S@A brief discussion of some of these methods used commonly for predictive analytics is provided below.@@@@1@16@@oe@26-8-2013
1000007201980@unknown@formal@none@1@S@A detailed study of machine learning can be found in Mitchell (1997).@@@@1@12@@oe@26-8-2013
1000007201990@unknown@formal@none@1@S@⌊=Neural networks¦4=⌋@@@@1@2@@oe@26-8-2013
1000007202000@unknown@formal@none@1@S@⌊>Neural networks>⌋ are ⌊>nonlinear>⌋ sophisticated modeling techniques that are able to ⌊>model>⌋ complex functions.@@@@1@14@@oe@26-8-2013
1000007202010@unknown@formal@none@1@S@They can be applied to problems of ⌊>prediction>⌋, ⌊>classification>⌋ or ⌊>control>⌋ in a wide spectrum of fields such as ⌊>finance>⌋, ⌊>cognitive psychology>⌋/⌊>neuroscience>⌋, ⌊>medicine>⌋, ⌊>engineering>⌋, and ⌊>physics>⌋.@@@@1@26@@oe@26-8-2013
1000007202020@unknown@formal@none@1@S@Neural networks are used when the exact nature of the relationship between inputs and output is not known.@@@@1@18@@oe@26-8-2013
1000007202030@unknown@formal@none@1@S@A key feature of neural networks is that they learn the relationship between inputs and output through training.@@@@1@18@@oe@26-8-2013
1000007202040@unknown@formal@none@1@S@There are two types of training in neural networks used by different networks, ⌊>supervised>⌋ and ⌊>unsupervised>⌋ training, with supervised being the most common one.@@@@1@24@@oe@26-8-2013
1000007202050@unknown@formal@none@1@S@Some examples of neural network training techniques are ⌊>backpropagation>⌋, quick propagation, ⌊>conjugate gradient descent>⌋, ⌊>projection operator>⌋, Delta-Bar-Delta etc.@@@@1@18@@oe@26-8-2013
1000007202060@unknown@formal@none@1@S@Theses are applied to network architectures such as multilayer ⌊>perceptron>⌋s, ⌊>Kohonen network>⌋s, ⌊>Hopfield network>⌋s, etc.@@@@1@15@@oe@26-8-2013
1000007202070@unknown@formal@none@1@S@⌊=Radial basis functions¦4=⌋@@@@1@3@@oe@26-8-2013
1000007202080@unknown@formal@none@1@S@A ⌊>radial basis function>⌋ (RBF) is a function which has built into it a distance criterion with respect to a center.@@@@1@21@@oe@26-8-2013
1000007202090@unknown@formal@none@1@S@Such functions can be used very efficiently for interpolation and for smoothing of data.@@@@1@14@@oe@26-8-2013
1000007202100@unknown@formal@none@1@S@Radial basis functions have been applied in the area of ⌊>neural network>⌋s where they are used as a replacement for the sigmoidal transfer function.@@@@1@24@@oe@26-8-2013
1000007202110@unknown@formal@none@1@S@Such networks have 3 layers, the input layer, the hidden layer with the RBF non-linearity and a linear output layer.@@@@1@20@@oe@26-8-2013
1000007202120@unknown@formal@none@1@S@The most popular choice for the non-linearity is the Gaussian.@@@@1@10@@oe@26-8-2013
1000007202130@unknown@formal@none@1@S@RBF networks have the advantage of not being locked into local minima as do the ⌊>feed-forward>⌋ networks such as the multilayer perceptron.@@@@1@22@@oe@26-8-2013
1000007202140@unknown@formal@none@1@S@⌊=Support vector machines¦4=⌋@@@@1@3@@oe@26-8-2013
1000007202150@unknown@formal@none@1@S@⌊>Support Vector Machine>⌋s (SVM) are used to detect and exploit complex patterns in data by clustering, classifying and ranking the data.@@@@1@21@@oe@26-8-2013
1000007202160@unknown@formal@none@1@S@They are learning machines that are used to perform binary classifications and regression estimations.@@@@1@14@@oe@26-8-2013
1000007202170@unknown@formal@none@1@S@They commonly use kernel based methods to apply linear classification techniques to non-linear classification problems.@@@@1@15@@oe@26-8-2013
1000007202180@unknown@formal@none@1@S@There are a number of types of SVM such as linear, polynomial, sigmoid etc.@@@@1@14@@oe@26-8-2013
1000007202190@unknown@formal@none@1@S@⌊=Naïve Bayes¦4=⌋@@@@1@2@@oe@26-8-2013
1000007202200@unknown@formal@none@1@S@⌊>Naïve Bayes>⌋ based on Bayes conditional probability rule is used for performing classification tasks.@@@@1@14@@oe@26-8-2013
1000007202210@unknown@formal@none@1@S@Naïve Bayes assumes the predictors are statistically independent which makes it an effective classification tool that is easy to interpret.@@@@1@20@@oe@26-8-2013
1000007202220@unknown@formal@none@1@S@It is best employed when faced with the problem of ‘curse of dimensionality’ i.e. when the number of predictors is very high.@@@@1@22@@oe@26-8-2013
1000007202230@unknown@formal@none@1@S@⌊=k-nearest neighbours¦4=⌋@@@@1@2@@oe@26-8-2013
1000007202240@unknown@formal@none@1@S@The ⌊>nearest neighbour algorithm>⌋ (KNN) belongs to the class of pattern recognition statistical methods.@@@@1@14@@oe@26-8-2013
1000007202250@unknown@formal@none@1@S@The method does not impose a priori any assumptions about the distribution from which the modeling sample is drawn.@@@@1@19@@oe@26-8-2013
1000007202260@unknown@formal@none@1@S@It involves a training set with both positive and negative values.@@@@1@11@@oe@26-8-2013
1000007202270@unknown@formal@none@1@S@A new sample is classified by calculating the distance to the nearest neighbouring training case.@@@@1@15@@oe@26-8-2013
1000007202280@unknown@formal@none@1@S@The sign of that point will determine the classification of the sample.@@@@1@12@@oe@26-8-2013
1000007202290@unknown@formal@none@1@S@In the k-nearest neighbour classifier, the k nearest points are considered and the sign of the majority is used to classify the sample.@@@@1@23@@oe@26-8-2013
1000007202300@unknown@formal@none@1@S@The performance of the kNN algorithm is influenced by three main factors: (1) the distance measure used to locate the nearest neighbours; (2) the decision rule used to derive a classification from the k-nearest neighbours; and (3) the number of neighbours used to classify the new sample.@@@@1@47@@oe@26-8-2013
1000007202310@unknown@formal@none@1@S@It can be proved that, unlike other methods, this method is universally asymptotically convergent, i.e.: as the size of the training set increases, if the observations are iid, regardless of the distribution from which the sample is drawn, the predicted class will converge to the class assignment that minimizes misclassification error.@@@@1@51@@oe@26-8-2013
1000007202320@unknown@formal@none@1@S@See Devroy et alt.@@@@1@4@@oe@26-8-2013
1000007202330@unknown@formal@none@1@S@⌊=Popular tools¦2=⌋@@@@1@2@@oe@26-8-2013
1000007202340@unknown@formal@none@1@S@There are numerous tools available in the marketplace which help with the execution of predictive analytics.@@@@1@16@@oe@26-8-2013
1000007202350@unknown@formal@none@1@S@These range from those which need very little user sophistication to those that are designed for the expert practitioner.@@@@1@19@@oe@26-8-2013
1000007202360@unknown@formal@none@1@S@The difference between these tools is often in the level of customization and heavy data lifting allowed.@@@@1@17@@oe@26-8-2013
1000007202370@unknown@formal@none@1@S@For traditional statistical modeling some of the popular tools are ⌊>DAP>⌋/⌊>SAS>⌋, S-Plus, ⌊>PSPP>⌋/⌊>SPSS>⌋ and Stata.@@@@1@15@@oe@26-8-2013
1000007202380@unknown@formal@none@1@S@For machine learning/data mining type of applications, KnowledgeSEEKER, KnowledgeSTUDIO, Enterprise Miner, GeneXproTools, ⌊>Viscovery>⌋, Clementine, ⌊>KXEN Analytic Framework>⌋, ⌊>InforSense>⌋ and Excel Miner are some of the popularly used options.@@@@1@28@@oe@26-8-2013
1000007202390@unknown@formal@none@1@S@Classification Tree analysis can be performed using CART software.@@@@1@9@@oe@26-8-2013
1000007202400@unknown@formal@none@1@S@SOMine is a predictive analytics tool based on ⌊>self-organizing map>⌋s (SOMs) available from ⌊>Viscovery Software>⌋.@@@@1@15@@oe@26-8-2013
1000007202410@unknown@formal@none@1@S@⌊>R>⌋ is a very powerful tool that can be used to perform almost any kind of statistical analysis, and is freely downloadable.@@@@1@22@@oe@26-8-2013
1000007202420@unknown@formal@none@1@S@⌊>WEKA>⌋ is a freely available ⌊>open-source>⌋ collection of ⌊>machine learning>⌋ methods for pattern classification, regression, clustering, and some types of meta-learning, which can be used for predictive analytics.@@@@1@28@@oe@26-8-2013
1000007202430@unknown@formal@none@1@S@⌊>RapidMiner>⌋ is another freely available integrated ⌊>open-source>⌋ software environment for predictive analytics, ⌊>data mining>⌋, and ⌊>machine learning>⌋ fully integrating WEKA and providing an even larger number of methods for predictive analytics.@@@@1@31@@oe@26-8-2013
1000007202440@unknown@formal@none@1@S@Recently, in an attempt to provide a standard language for expressing predictive models, the ⌊>Predictive Model Markup Language>⌋ (PMML) has been proposed.@@@@1@22@@oe@26-8-2013
1000007202450@unknown@formal@none@1@S@Such an XML-based language provides a way for the different tools to define predictive models and to share these between PMML compliant applications.@@@@1@23@@oe@26-8-2013
1000007202460@unknown@formal@none@1@S@Several tools already produce or consume PMML documents, these include ⌊>ADAPA>⌋, ⌊>IBM DB2>⌋ Warehouse, CART, SAS Enterprise Miner, and ⌊>SPSS>⌋.@@@@1@20@@oe@26-8-2013
1000007202470@unknown@formal@none@1@S@Predictive analytics has also found its way into the IT lexicon, most notably in the area of IT Automation.@@@@1@19@@oe@26-8-2013
1000007202480@unknown@formal@none@1@S@Vendors such as ⌊>Stratavia>⌋ and their ⌊>Data Palette>⌋ product offer predictive analytics as part of their automation platform, predicting how resources will behave in the future and automate the environment accordingly.@@@@1@31@@oe@26-8-2013
1000007202490@unknown@formal@none@1@S@The widespread use of predictive analytics in industry has led to the proliferation of numerous productized solutions firms.@@@@1@18@@oe@26-8-2013
1000007202500@unknown@formal@none@1@S@Some of them are highly specialized (focusing, for example, on fraud detection, automatic saleslead generation or response modeling) in a specific domain (⌊>Fair Isaac>⌋ for credit card scores) or industry verticals (MarketRx in Pharmaceutical).@@@@1@34@@oe@26-8-2013
1000007202510@unknown@formal@none@1@S@Others provide predictive analytics services in support of a wide range of business problems across industry verticals (⌊>Fifth C>⌋).@@@@1@19@@oe@26-8-2013
1000007202520@unknown@formal@none@1@S@Predictive Analytics competitions are also fairly common and often pit academics and Industry practitioners (see for example, KDD CUP).@@@@1@19@@oe@26-8-2013
1000007202530@unknown@formal@none@1@S@⌊=Conclusion¦2=⌋@@@@1@1@@oe@26-8-2013
1000007202540@unknown@formal@none@1@S@Predictive analytics adds great value to a businesses decision making capabilities by allowing it to formulate smart policies on the basis of predictions of future outcomes.@@@@1@26@@oe@26-8-2013
1000007202550@unknown@formal@none@1@S@A broad range of tools and techniques are available for this type of analysis and their selection is determined by the analytical maturity of the firm as well as the specific requirements of the problem being solved.@@@@1@37@@oe@26-8-2013
1000007202560@unknown@formal@none@1@S@⌊=Education¦2=⌋@@@@1@1@@oe@26-8-2013
1000007202570@unknown@formal@none@1@S@Predictive analytics is taught at the following institutions:@@@@1@8@@oe@26-8-2013
1000007202580@unknown@formal@none@1@S@⌊•⌊#Ghent University, Belgium: ⌊> Master of Marketing Analysis>⌋, an 8-month advanced master degree taught in English with strong emphasis on applications of predictive analytics in Analytical CRM.#⌋•⌋@@@@1@27@@oe@26-8-2013
1000007300010@unknown@formal@none@1@S@⌊δRapidMinerδ⌋@@@@1@1@@oe@26-8-2013
1000007300020@unknown@formal@none@1@S@⌊∗RapidMiner∗⌋ (formerly YALE (Yet Another Learning Environment)) is an environment for ⌊>machine learning>⌋ and ⌊>data mining>⌋ experiments.@@@@1@17@@oe@26-8-2013
1000007300030@unknown@formal@none@1@S@It allows experiments to be made up of a large number of arbitrarily nestable operators, described in ⌊>XML>⌋ files which can easily be created with RapidMiner's ⌊>graphical user interface>⌋.@@@@1@29@@oe@26-8-2013
1000007300040@unknown@formal@none@1@S@Applications of RapidMiner cover both research and real-world data mining tasks.@@@@1@11@@oe@26-8-2013
1000007300050@unknown@formal@none@1@S@The initial version has been developed by the Artificial Intelligence Unit of ⌊>University of Dortmund>⌋ since ⌊>2001>⌋.@@@@1@17@@oe@26-8-2013
1000007300060@unknown@formal@none@1@S@It is distributed under a ⌊>GNU>⌋ license, and has been hosted by ⌊>SourceForge>⌋ since ⌊>2004>⌋.@@@@1@15@@oe@26-8-2013
1000007300070@unknown@formal@none@1@S@RapidMiner provides more than 400 operators for all main machine learning procedures, including input and output, and data preprocessing and visualization.@@@@1@21@@oe@26-8-2013
1000007300080@unknown@formal@none@1@S@It is written in the ⌊>Java programming language>⌋ and therefore can work on all popular operating systems.@@@@1@17@@oe@26-8-2013
1000007300090@unknown@formal@none@1@S@It also integrates all learning schemes and attribute evaluators of the ⌊>Weka>⌋ learning environment.@@@@1@14@@oe@26-8-2013
1000007300100@unknown@formal@none@1@S@⌊=Properties¦2=⌋@@@@1@1@@oe@26-8-2013
1000007300110@unknown@formal@none@1@S@Some properties of RapidMiner are:@@@@1@5@@oe@26-8-2013
1000007300120@unknown@formal@none@1@S@⌊•⌊#written in Java#⌋@@@@1@3@@oe@26-8-2013
1000007300130@unknown@formal@none@1@S@⌊#⌊>knowledge discovery>⌋ processes are modeled as operator trees#⌋@@@@1@8@@oe@26-8-2013
1000007300140@unknown@formal@none@1@S@⌊#internal XML representation ensures standardized interchange format of data mining experiments#⌋@@@@1@11@@oe@26-8-2013
1000007300150@unknown@formal@none@1@S@⌊#scripting language allows for automatic large-scale experiments#⌋@@@@1@7@@oe@26-8-2013
1000007300160@unknown@formal@none@1@S@⌊#multi-layered data view concept ensures efficient and transparent data handling#⌋@@@@1@10@@oe@26-8-2013
1000007300170@unknown@formal@none@1@S@⌊#⌊>graphical user interface>⌋, ⌊>command line>⌋ mode (⌊>batch mode>⌋), and ⌊>Java API>⌋ for using RapidMiner from your own programs#⌋@@@@1@18@@oe@26-8-2013
1000007300180@unknown@formal@none@1@S@⌊#⌊>plugin>⌋ and ⌊>extension>⌋ mechanisms, several plugins already exist#⌋@@@@1@8@@oe@26-8-2013
1000007300190@unknown@formal@none@1@S@⌊#⌊>plotting>⌋ facility offering a large set of high-dimensional visualization schemes for data and models#⌋@@@@1@14@@oe@26-8-2013
1000007300200@unknown@formal@none@1@S@⌊#applications include ⌊>text mining>⌋, multimedia mining, feature engineering, data stream mining and tracking drifting concepts, development of ensemble methods, and distributed data mining.#⌋•⌋@@@@1@23@@oe@26-8-2013
1000007400010@unknown@formal@none@1@S@⌊δRussian languageδ⌋@@@@1@2@@oe@26-8-2013
1000007400020@unknown@formal@none@1@S@⌊∗Russian∗⌋ (⌊>⌊λрусский язык¦ru¦русский язык¦Langλ⌋>⌋ ⌊↓(↓⌋⌊>⌊↓help↓⌋>⌋⌊↓•↓⌋⌊>⌊↓info↓⌋>⌋⌊↓)↓⌋, ⌊>transliteration>⌋: ⌊/⌊λrusskiy yazyk¦ru¦ALA¦russkiy yazyk¦Translλ⌋/⌋, ⌊λ⌊↓Russian pronunciation:↓⌋ ⌊>[ˈruskʲɪj jɪˈzɨk>⌋]¦ˈruskʲɪj jɪˈzɨk¦IPA-ruλ⌋) is the most geographically widespread language of ⌊>Eurasia>⌋, the most widely spoken of the ⌊>Slavic languages>⌋, and the largest ⌊>native language>⌋ in ⌊>Europe>⌋.@@@@1@36@@oe@26-8-2013
1000007400030@unknown@formal@none@1@S@Russian belongs to the family of ⌊>Indo-European languages>⌋ and is one of three (or, according to some authorities , four) living members of the ⌊>East Slavic languages>⌋, the others being ⌊>Belarusian>⌋ and ⌊>Ukrainian>⌋ (and possibly ⌊>Rusyn>⌋, often considered a dialect of Ukrainian).@@@@1@42@@oe@26-8-2013
1000007400040@unknown@formal@none@1@S@It is also spoken by the countries of the ⌊>Russophone>⌋.@@@@1@10@@oe@26-8-2013
1000007400050@unknown@formal@none@1@S@Written examples of Old East Slavonic are attested from the 10th century onwards.@@@@1@13@@oe@26-8-2013
1000007400060@unknown@formal@none@1@S@Today Russian is widely used outside ⌊>Russia>⌋.@@@@1@7@@oe@26-8-2013
1000007400070@unknown@formal@none@1@S@It is applied as a means of coding and storage of universal knowledge — 60–70% of all world information is published in English and Russian languages.@@@@1@26@@oe@26-8-2013
1000007400080@unknown@formal@none@1@S@Over a quarter of the world's scientific literature is published in Russian.@@@@1@12@@oe@26-8-2013
1000007400090@unknown@formal@none@1@S@Russian is also a necessary accessory of world communications systems (broadcasts, air- and space communication, etc).@@@@1@16@@oe@26-8-2013
1000007400100@unknown@formal@none@1@S@Due to the status of the ⌊>Soviet Union>⌋ as a ⌊>superpower>⌋, Russian had great political importance in the 20th century.@@@@1@20@@oe@26-8-2013
1000007400110@unknown@formal@none@1@S@Hence, the language is one of the ⌊>official languages>⌋ of the ⌊>United Nations>⌋.@@@@1@13@@oe@26-8-2013
1000007400120@unknown@formal@none@1@S@Russian distinguishes between ⌊>consonant>⌋ ⌊>phoneme>⌋s with ⌊>palatal>⌋ ⌊>secondary articulation>⌋ and those without, the so-called ⌊/soft/⌋ and ⌊/hard/⌋ sounds.@@@@1@18@@oe@26-8-2013
1000007400130@unknown@formal@none@1@S@This distinction is found between pairs of almost all consonants and is one of the most distinguishing features of the language.@@@@1@21@@oe@26-8-2013
1000007400140@unknown@formal@none@1@S@Another important aspect is the ⌊>reduction>⌋ of ⌊>unstressed>⌋ ⌊>vowel>⌋s, which is somewhat similar to ⌊>that of English>⌋.@@@@1@17@@oe@26-8-2013
1000007400150@unknown@formal@none@1@S@Stress, which is unpredictable, is not normally indicated orthographically.@@@@1@9@@oe@26-8-2013
1000007400160@unknown@formal@none@1@S@According to the Institute of Russian Language of the Russian Academy of Sciences, an optional ⌊>acute accent>⌋ (⌊/⌊λзнак ударения¦ru¦знак ударения¦Langλ⌋/⌋) may, and sometimes should, be used to mark stress.@@@@1@29@@oe@26-8-2013
1000007400170@unknown@formal@none@1@S@For example, it is used to distinguish between otherwise identical words, especially when context doesn't make it obvious: ⌊/замо́к/за́мок/⌋ (lock/castle), ⌊/сто́ящий/стоя́щий/⌋ (worthwhile/standing), ⌊/чудно́/чу́дно/⌋ (this is odd/this is marvellous), ⌊/молоде́ц/мо́лодец/⌋ (attaboy/fine young man), ⌊/узна́ю/узнаю́/⌋ (I shall learn it/I am learning it), ⌊/отреза́ть/отре́зать/⌋ (infinitive for "cut"/perfective for "cut"); to indicate the proper pronouncation of uncommon words, especially personal and family names (⌊/афе́ра, гу́ру, Гарси́а, Оле́ша, Фе́рми/⌋), and to express the stressed word in the sentence (⌊/Ты́ съел печенье?/Ты съе́л печенье?/Ты съел пече́нье?/⌋ - Was it you who eat the cookie?/Did you eat the cookie?/Was the cookie your meal?).@@@@1@96@@oe@26-8-2013
1000007400180@unknown@formal@none@1@S@Acute accents are mandatory in lexical dictionaries and books intended to be used either by children or foreign readers.@@@@1@19@@oe@26-8-2013
1000007400190@unknown@formal@none@1@S@⌊=Classification¦2=⌋@@@@1@1@@oe@26-8-2013
1000007400200@unknown@formal@none@1@S@Russian is a ⌊>Slavic language>⌋ in the ⌊>Indo-European family>⌋.@@@@1@9@@oe@26-8-2013
1000007400210@unknown@formal@none@1@S@From the point of view of the ⌊>spoken language>⌋, its closest relatives are ⌊>Ukrainian>⌋ and ⌊>Belarusian>⌋, the other two national languages in the ⌊>East Slavic>⌋ group.@@@@1@26@@oe@26-8-2013
1000007400220@unknown@formal@none@1@S@In many places in eastern ⌊>Ukraine>⌋ and ⌊>Belarus>⌋, these languages are spoken interchangeably, and in certain areas traditional bilingualism resulted in language mixture, e.g. ⌊>Surzhyk>⌋ in eastern Ukraine and ⌊>Trasianka>⌋ in Belarus.@@@@1@32@@oe@26-8-2013
1000007400230@unknown@formal@none@1@S@An East Slavic ⌊>Old Novgorod dialect>⌋, although vanished during the fifteenth or sixteenth century, is sometimes considered to have played a significant role in formation of the modern Russian language.@@@@1@30@@oe@26-8-2013
1000007400240@unknown@formal@none@1@S@The vocabulary (mainly abstract and literary words), principles of word formation, and, to some extent, inflections and literary style of Russian have been also influenced by ⌊>Church Slavonic>⌋, a developed and partly adopted form of the ⌊>South Slavic>⌋ ⌊>Old Church Slavonic>⌋ language used by the ⌊>Russian Orthodox Church>⌋.@@@@1@48@@oe@26-8-2013
1000007400250@unknown@formal@none@1@S@However, the East Slavic forms have tended to be used exclusively in the various dialects that are experiencing a rapid decline.@@@@1@21@@oe@26-8-2013
1000007400260@unknown@formal@none@1@S@In some cases, both the ⌊>East Slavic>⌋ and the ⌊>Church Slavonic>⌋ forms are in use, with slightly different meanings.@@@@1@19@@oe@26-8-2013
1000007400270@unknown@formal@none@1@S@⌊/For details, see ⌊>Russian phonology>⌋ and ⌊>History of the Russian language>⌋./⌋@@@@1@11@@oe@26-8-2013
1000007400280@unknown@formal@none@1@S@Russian phonology and syntax (especially in northern dialects) have also been influenced to some extent by the numerous Finnic languages of the ⌊>Finno-Ugric subfamily>⌋: ⌊>Merya>⌋, ⌊>Moksha>⌋, ⌊>Muromian>⌋, the language of the ⌊>Meshchera>⌋, ⌊>Veps>⌋, et cetera.@@@@1@35@@oe@26-8-2013
1000007400290@unknown@formal@none@1@S@These languages, some of them now extinct, used to be spoken in the center and in the north of what is now the European part of Russia.@@@@1@27@@oe@26-8-2013
1000007400300@unknown@formal@none@1@S@They came in contact with Eastern Slavic as far back as the early Middle Ages and eventually served as substratum for the modern Russian language.@@@@1@25@@oe@26-8-2013
1000007400310@unknown@formal@none@1@S@The Russian dialects spoken north, north-east and north-west of ⌊>Moscow>⌋ have a considerable number of words of Finno-Ugric origin.@@@@1@19@@oe@26-8-2013
1000007400320@unknown@formal@none@1@S@Over the course of centuries, the vocabulary and literary style of Russian have also been influenced by Turkic/Caucasian/Central Asian languages, as well as Western/Central European languages such as ⌊>Polish>⌋, ⌊>Latin>⌋, ⌊>Dutch>⌋, ⌊>German>⌋, ⌊>French>⌋, and ⌊>English>⌋.@@@@1@35@@oe@26-8-2013
1000007400330@unknown@formal@none@1@S@According to the ⌊>Defense Language Institute>⌋ in ⌊>Monterey, California>⌋, Russian is classified as a level III language in terms of learning difficulty for native English speakers, requiring approximately 780 hours of immersion instruction to achieve intermediate fluency.@@@@1@37@@oe@26-8-2013
1000007400340@unknown@formal@none@1@S@It is also regarded by the ⌊>United States Intelligence Community>⌋ as a "hard target" language, due to both its difficulty to master for English speakers as well as due to its critical role in American world policy.@@@@1@37@@oe@26-8-2013
1000007400350@unknown@formal@none@1@S@⌊=Geographic distribution¦2=⌋@@@@1@2@@oe@26-8-2013
1000007400360@unknown@formal@none@1@S@Russian is primarily spoken in ⌊>Russia>⌋ and, to a lesser extent, the other countries that were once constituent republics of the ⌊>USSR>⌋.@@@@1@22@@oe@26-8-2013
1000007400370@unknown@formal@none@1@S@Until ⌊>1917>⌋, it was the sole official language of the ⌊>Russian Empire>⌋.@@@@1@12@@oe@26-8-2013
1000007400380@unknown@formal@none@1@S@During the Soviet period, the policy toward the languages of the various other ethnic groups fluctuated in practice.@@@@1@18@@oe@26-8-2013
1000007400390@unknown@formal@none@1@S@Though each of the constituent republics had its own official language, the unifying role and superior status was reserved for Russian.@@@@1@21@@oe@26-8-2013
1000007400400@unknown@formal@none@1@S@Following the break-up of ⌊>1991>⌋, several of the newly independent states have encouraged their native languages, which has partly reversed the privileged status of Russian, though its role as the language of post-Soviet national intercourse throughout the region has continued.@@@@1@40@@oe@26-8-2013
1000007400410@unknown@formal@none@1@S@In ⌊>Latvia>⌋, notably, its official recognition and legality in the classroom have been a topic of considerable debate in a country where more than one-third of the population is Russian-speaking, consisting mostly of post-⌊>World War II>⌋ immigrants from Russia and other parts of the former ⌊>USSR>⌋ (Belarus, Ukraine).@@@@1@48@@oe@26-8-2013
1000007400420@unknown@formal@none@1@S@Similarly, in ⌊>Estonia>⌋, the Soviet-era immigrants and their Russian-speaking descendants constitute 25,6% of the country's current population and 58,6% of the native Estonian population is also able to speak Russian.@@@@1@30@@oe@26-8-2013
1000007400430@unknown@formal@none@1@S@In all, 67,8% of Estonia's population can speak Russian.@@@@1@9@@oe@26-8-2013
1000007400440@unknown@formal@none@1@S@In ⌊>Kazakhstan>⌋ and ⌊>Kyrgyzstan>⌋, Russian remains a co-official language with ⌊>Kazakh>⌋ and ⌊>Kyrgyz>⌋ respectively.@@@@1@14@@oe@26-8-2013
1000007400450@unknown@formal@none@1@S@Large Russian-speaking communities still exist in northern Kazakhstan, and ethnic Russians comprise 25.6 % of Kazakhstan's population.@@@@1@17@@oe@26-8-2013
1000007400460@unknown@formal@none@1@S@A much smaller Russian-speaking minority in ⌊>Lithuania>⌋ has represented less than 1/10 of the country's overall population.@@@@1@17@@oe@26-8-2013
1000007400470@unknown@formal@none@1@S@Nevertheless more than half of the population of the ⌊>Baltic states>⌋ are able to hold a conversation in Russian and almost all have at least some familiarity with the most basic spoken and written phrases.@@@@1@35@@oe@26-8-2013
1000007400480@unknown@formal@none@1@S@The Russian control of ⌊>Finland>⌋ in 1809–1918, however, has left few Russian speakers in Finland.@@@@1@15@@oe@26-8-2013
1000007400490@unknown@formal@none@1@S@There are 33,400 Russian speakers in Finland, amounting to 0.6% of the population.@@@@1@13@@oe@26-8-2013
1000007400500@unknown@formal@none@1@S@5000 (0.1%) of them are late 19th century and 20th century immigrants, and the rest are recent immigrants, who have arrived in the 90's and later.@@@@1@26@@oe@26-8-2013
1000007400510@unknown@formal@none@1@S@In the twentieth century, Russian was widely taught in the schools of the members of the old ⌊>Warsaw Pact>⌋ and in other ⌊>countries>⌋ that used to be allies of the USSR.@@@@1@31@@oe@26-8-2013
1000007400520@unknown@formal@none@1@S@In particular, these countries include ⌊>Poland>⌋, ⌊>Bulgaria>⌋, the ⌊>Czech Republic>⌋, ⌊>Slovakia>⌋, ⌊>Hungary>⌋, ⌊>Romania>⌋, ⌊>Albania>⌋ and ⌊>Cuba>⌋.@@@@1@16@@oe@26-8-2013
1000007400530@unknown@formal@none@1@S@However, younger generations are usually not fluent in it, because Russian is no longer mandatory in the school system.@@@@1@19@@oe@26-8-2013
1000007400540@unknown@formal@none@1@S@It is currently the most widely-taught foreign language in ⌊>Mongolia>⌋.@@@@1@10@@oe@26-8-2013
1000007400550@unknown@formal@none@1@S@Russian is also spoken in ⌊>Israel>⌋ by at least 750,000 ethnic ⌊>Jew>⌋ish immigrants from the former ⌊>Soviet Union>⌋ (1999 census).@@@@1@20@@oe@26-8-2013
1000007400560@unknown@formal@none@1@S@The Israeli ⌊>press>⌋ and ⌊>website>⌋s regularly publish material in Russian.@@@@1@10@@oe@26-8-2013
1000007400570@unknown@formal@none@1@S@Sizable Russian-speaking communities also exist in ⌊>North America>⌋, especially in large urban centers of the ⌊>U.S.>⌋ and ⌊>Canada>⌋ such as ⌊>New York City>⌋, ⌊>Philadelphia>⌋, ⌊>Boston>⌋, ⌊>Los Angeles>⌋, ⌊>San Francisco>⌋, ⌊>Seattle>⌋, ⌊>Toronto>⌋, ⌊>Baltimore>⌋, ⌊>Miami>⌋, ⌊>Chicago>⌋, ⌊>Denver>⌋, and the ⌊>Cleveland>⌋ suburb of ⌊>Richmond Heights>⌋.@@@@1@42@@oe@26-8-2013
1000007400580@unknown@formal@none@1@S@In the former two, Russian-speaking groups total over half a million.@@@@1@11@@oe@26-8-2013
1000007400590@unknown@formal@none@1@S@In a number of locations they issue their own newspapers, and live in their self-sufficient neighborhoods (especially the generation of immigrants who started arriving in the early sixties).@@@@1@28@@oe@26-8-2013
1000007400600@unknown@formal@none@1@S@Only about a quarter of them are ethnic Russians, however.@@@@1@10@@oe@26-8-2013
1000007400610@unknown@formal@none@1@S@Before the ⌊>dissolution of the Soviet Union>⌋, the overwhelming majority of ⌊>Russophone>⌋s in North America were Russian-speaking ⌊>Jews>⌋.@@@@1@18@@oe@26-8-2013
1000007400620@unknown@formal@none@1@S@Afterwards the influx from the countries of the former ⌊>Soviet Union>⌋ changed the statistics somewhat.@@@@1@15@@oe@26-8-2013
1000007400630@unknown@formal@none@1@S@According to the ⌊>United States 2000 Census>⌋, Russian is the primary language spoken in the homes of over 700,000 individuals living in the United States.@@@@1@25@@oe@26-8-2013
1000007400640@unknown@formal@none@1@S@Significant Russian-speaking groups also exist in ⌊>Western Europe>⌋.@@@@1@8@@oe@26-8-2013
1000007400650@unknown@formal@none@1@S@These have been fed by several waves of immigrants since the beginning of the twentieth century, each with its own flavor of language.@@@@1@23@@oe@26-8-2013
1000007400660@unknown@formal@none@1@S@⌊>Germany>⌋, the ⌊>United Kingdom>⌋, ⌊>Spain>⌋, ⌊>France>⌋, ⌊>Italy>⌋, ⌊>Belgium>⌋, ⌊>Greece>⌋, ⌊>Brazil>⌋, ⌊>Norway>⌋, ⌊>Austria>⌋, and ⌊>Turkey>⌋ have significant Russian-speaking communities totaling 3 million people.@@@@1@22@@oe@26-8-2013
1000007400670@unknown@formal@none@1@S@Two thirds of them are actually Russian-speaking descendants of ⌊>Germans>⌋, ⌊>Greeks>⌋, ⌊>Jews>⌋, ⌊>Armenians>⌋, or ⌊>Ukrainians>⌋ who either repatriated after the ⌊>USSR>⌋ collapsed or are just looking for temporary employment.@@@@1@29@@oe@26-8-2013
1000007400680@unknown@formal@none@1@S@Recent estimates of the total number of speakers of Russian:@@@@1@10@@oe@26-8-2013
1000007400690@unknown@formal@none@1@S@⌊=Official status¦3=⌋@@@@1@2@@oe@26-8-2013
1000007400700@unknown@formal@none@1@S@Russian is the official language of ⌊>Russia>⌋.@@@@1@7@@oe@26-8-2013
1000007400710@unknown@formal@none@1@S@It is also an official language of ⌊>Belarus>⌋, ⌊>Kazakhstan>⌋, ⌊>Kyrgyzstan>⌋, an unofficial but widely spoken language in ⌊>Ukraine>⌋ and the de facto official language of the ⌊>unrecognized>⌋ of ⌊>Transnistria>⌋, ⌊>South Ossetia>⌋ and ⌊>Abkhazia>⌋.@@@@1@33@@oe@26-8-2013
1000007400720@unknown@formal@none@1@S@Russian is one of the ⌊>six official languages>⌋ of the ⌊>United Nations>⌋.@@@@1@12@@oe@26-8-2013
1000007400730@unknown@formal@none@1@S@Education in Russian is still a popular choice for both Russian as a second language (RSL) and native speakers in Russia as well as many of the former Soviet republics.@@@@1@30@@oe@26-8-2013
1000007400740@unknown@formal@none@1@S@97% of the public school students of Russia, 75% in Belarus, 41% in Kazakhstan, 25% in ⌊>Ukraine>⌋, 23% in Kyrgyzstan, 21% in ⌊>Moldova>⌋, 7% in ⌊>Azerbaijan>⌋, 5% in ⌊>Georgia>⌋ and 2% in ⌊>Armenia>⌋ and ⌊>Tajikistan>⌋ receive their education only or mostly in Russian.@@@@1@43@@oe@26-8-2013
1000007400750@unknown@formal@none@1@S@Although the corresponding percentage of ethnic Russians is 78% in ⌊>Russia>⌋, 10% in ⌊>Belarus>⌋, 26% in ⌊>Kazakhstan>⌋, 17% in ⌊>Ukraine>⌋, 9% in ⌊>Kyrgyzstan>⌋, 6% in ⌊>Moldova>⌋, 2% in ⌊>Azerbaijan>⌋, 1.5% in ⌊>Georgia>⌋ and less than 1% in both ⌊>Armenia>⌋ and ⌊>Tajikistan>⌋.@@@@1@41@@oe@26-8-2013
1000007400760@unknown@formal@none@1@S@Russian-language schooling is also available in Latvia, Estonia and Lithuania, but due to education reforms, a number of subjects taught in Russian are reduced at the high school level.@@@@1@29@@oe@26-8-2013
1000007400770@unknown@formal@none@1@S@The language has a co-official status alongside ⌊>Moldovan>⌋ in the autonomies of ⌊>Gagauzia>⌋ and ⌊>Transnistria>⌋ in ⌊>Moldova>⌋, and in seven ⌊>Romania>⌋n ⌊>communes>⌋ in ⌊>Tulcea>⌋ and ⌊>Constanţa>⌋ counties.@@@@1@27@@oe@26-8-2013
1000007400780@unknown@formal@none@1@S@In these localities, Russian-speaking ⌊>Lipovans>⌋, who are a recognized ethnic minority, make up more than 20% of the population.@@@@1@19@@oe@26-8-2013
1000007400790@unknown@formal@none@1@S@Thus, according to Romania's minority rights law, education, signage, and access to public administration and the justice system are provided in Russian alongside Romanian.@@@@1@24@@oe@26-8-2013
1000007400800@unknown@formal@none@1@S@In the ⌊>Autonomous Republic of Crimea>⌋ in Ukraine, Russian is an officially recognized language alongside with ⌊>Crimean Tatar>⌋, but in reality, is the only language used by the government, thus being a ⌊/⌊>de facto>⌋/⌋ official language.@@@@1@36@@oe@26-8-2013
1000007400810@unknown@formal@none@1@S@⌊=Dialects¦3=⌋@@@@1@1@@oe@26-8-2013
1000007400820@unknown@formal@none@1@S@Despite leveling after 1900, especially in matters of vocabulary, a number of dialects exist in Russia.@@@@1@16@@oe@26-8-2013
1000007400830@unknown@formal@none@1@S@Some linguists divide the dialects of the Russian language into two primary regional groupings, "Northern" and "Southern", with ⌊>Moscow>⌋ lying on the zone of transition between the two.@@@@1@28@@oe@26-8-2013
1000007400840@unknown@formal@none@1@S@Others divide the language into three groupings, Northern, Central and Southern, with Moscow lying in the Central region.@@@@1@18@@oe@26-8-2013
1000007400850@unknown@formal@none@1@S@⌊>Dialectology>⌋ within Russia recognizes dozens of smaller-scale variants.@@@@1@8@@oe@26-8-2013
1000007400860@unknown@formal@none@1@S@The dialects often show distinct and non-standard features of pronunciation and intonation, vocabulary, and grammar.@@@@1@15@@oe@26-8-2013
1000007400870@unknown@formal@none@1@S@Some of these are relics of ancient usage now completely discarded by the standard language.@@@@1@15@@oe@26-8-2013
1000007400880@unknown@formal@none@1@S@The ⌊>northern Russian dialects>⌋ and those spoken along the ⌊>Volga River>⌋ typically pronounce unstressed ⌊λ/o/¦/o/¦IPAλ⌋ clearly (the phenomenon called ⌊>okanye>⌋/оканье).@@@@1@20@@oe@26-8-2013
1000007400890@unknown@formal@none@1@S@East of Moscow, particularly in ⌊>Ryazan Region>⌋, unstressed ⌊λ/e/¦/e/¦IPAλ⌋ and ⌊λ/a/¦/a/¦IPAλ⌋ following ⌊>palatalized>⌋ consonants and preceding a stressed syllable are not reduced to ⌊λ[ɪ]¦[ɪ]¦IPAλ⌋ (like in the Moscow dialect), being instead pronounced as ⌊λ/a/¦/a/¦IPAλ⌋ in such positions (e.g. несл⌊∗и∗⌋ is pronounced as ⌊λ[nʲasˈlʲi]¦[nʲasˈlʲi]¦IPAλ⌋, not as ⌊λ[nʲɪsˈlʲi]¦[nʲɪsˈlʲi]¦IPAλ⌋) - this is called ⌊>yakanye>⌋/ яканье; many southern dialects have a palatalized final ⌊λ/tʲ/¦/tʲ/¦IPAλ⌋ in 3rd person forms of verbs (this is unpalatalized in the standard dialect) and a fricative ⌊λ[ɣ]¦[ɣ]¦IPAλ⌋ where the standard dialect has ⌊λ[g]¦[g]¦IPAλ⌋.@@@@1@83@@oe@26-8-2013
1000007400900@unknown@formal@none@1@S@However, in certain areas south of Moscow, e.g. in and around ⌊>Tula>⌋, ⌊λ/g/¦/g/¦IPAλ⌋ is pronounced as in the Moscow and northern dialects unless it precedes a voiceless plosive or a pause.@@@@1@31@@oe@26-8-2013
1000007400910@unknown@formal@none@1@S@In this position ⌊λ/g/¦/g/¦IPAλ⌋ is lenited and devoiced to the fricative ⌊λ[x]¦[x]¦IPAλ⌋, e.g. друг ⌊λ[drux]¦[drux]¦IPAλ⌋ (in Moscow's dialect, only Бог ⌊λ[box]¦[box]¦IPAλ⌋, лёгкий ⌊λ[lʲɵxʲkʲɪj]¦[lʲɵxʲkʲɪj]¦IPAλ⌋, мягкий ⌊λ[ˈmʲæxʲkʲɪj]¦[ˈmʲæxʲkʲɪj]¦IPAλ⌋ and some derivatives follow this rule).@@@@1@31@@oe@26-8-2013
1000007400920@unknown@formal@none@1@S@Some of these features (e.g. a ⌊>debuccalized>⌋ or ⌊>lenited>⌋ ⌊λ/g/¦/g/¦IPAλ⌋ and palatalized final ⌊λ/tʲ/¦/tʲ/¦IPAλ⌋ in 3rd person forms of verbs) are also present in modern ⌊>Ukrainian>⌋, indicating either a linguistic continuum or strong influence one way or the other.@@@@1@39@@oe@26-8-2013
1000007400930@unknown@formal@none@1@S@The city of ⌊>Veliky Novgorod>⌋ has historically displayed a feature called chokanye/tsokanye (чоканье/цоканье), where ⌊λ/ʨ/¦/ʨ/¦IPAλ⌋ and ⌊λ/ʦ/¦/ʦ/¦IPAλ⌋ were confused (this is thought to be due to influence from ⌊>Finnish>⌋, which doesn't distinguish these sounds).@@@@1@34@@oe@26-8-2013
1000007400940@unknown@formal@none@1@S@So, ⌊∗ц∗⌋апля ("heron") has been recorded as 'чапля'.@@@@1@8@@oe@26-8-2013
1000007400950@unknown@formal@none@1@S@Also, the second palatalization of ⌊>velar>⌋s did not occur there, so the so-called ⌊∗ě²∗⌋ (from the Proto-Slavonic diphthong *ai) did not cause ⌊λ/k, g, x/¦/k, g, x/¦IPAλ⌋ to shift to ⌊λ/ʦ, ʣ, s/¦/ʦ, ʣ, s/¦IPAλ⌋; therefore where ⌊>Standard Russian>⌋ has ⌊∗ц∗⌋епь ("chain"), the form ⌊∗к∗⌋епь ⌊λ[kʲepʲ]¦[kʲepʲ]¦IPAλ⌋ is attested in earlier texts.@@@@1@51@@oe@26-8-2013
1000007400960@unknown@formal@none@1@S@Among the first to study Russian dialects was ⌊>Lomonosov>⌋ in the eighteenth century.@@@@1@13@@oe@26-8-2013
1000007400970@unknown@formal@none@1@S@In the nineteenth, ⌊>Vladimir Dal>⌋ compiled the first dictionary that included dialectal vocabulary.@@@@1@13@@oe@26-8-2013
1000007400980@unknown@formal@none@1@S@Detailed mapping of Russian dialects began at the turn of the twentieth century.@@@@1@13@@oe@26-8-2013
1000007400990@unknown@formal@none@1@S@In modern times, the monumental ⌊/Dialectological Atlas of the Russian Language/⌋ (⌊/Диалектологический атлас русского языка/⌋ ⌊λ[dʲɪɐˌlʲɛktəlɐˈgʲiʨɪskʲɪj ˈatləs ˈruskəvə jɪzɨˈka]¦[dʲɪɐˌlʲɛktəlɐˈgʲiʨɪskʲɪj ˈatləs ˈruskəvə jɪzɨˈka]¦IPAλ⌋), was published in 3 folio volumes 1986–1989, after four decades of preparatory work.@@@@1@35@@oe@26-8-2013
1000007401000@unknown@formal@none@1@S@The ⌊/standard language/⌋ is based on (but not identical to) the Moscow dialect.@@@@1@13@@oe@26-8-2013
1000007401010@unknown@formal@none@1@S@⌊=Derived languages¦3=⌋@@@@1@2@@oe@26-8-2013
1000007401020@unknown@formal@none@1@S@⌊•⌊#⌊>Balachka>⌋ a dialect, spoken primarily by ⌊>Cossacks>⌋, in the regions of Don, ⌊>Kuban>⌋ and ⌊>Terek>⌋.#⌋@@@@1@15@@oe@26-8-2013
1000007401030@unknown@formal@none@1@S@⌊#⌊>Fenya>⌋, a criminal ⌊>argot>⌋ of ancient origin, with Russian grammar, but with distinct vocabulary.#⌋@@@@1@14@@oe@26-8-2013
1000007401040@unknown@formal@none@1@S@⌊#⌊>Nadsat>⌋, the fictional language spoken in '⌊>A Clockwork Orange>⌋' uses a lot of Russian words and Russian slang.#⌋@@@@1@18@@oe@26-8-2013
1000007401050@unknown@formal@none@1@S@⌊#⌊>Surzhyk>⌋ is a language with Russian and Ukrainian features, spoken in some areas of Ukraine#⌋@@@@1@15@@oe@26-8-2013
1000007401060@unknown@formal@none@1@S@⌊#⌊>Trasianka>⌋ is a language with Russian and Belarusian features used by a large portion of the rural population in ⌊>Belarus>⌋.#⌋@@@@1@20@@oe@26-8-2013
1000007401070@unknown@formal@none@1@S@⌊#⌊>Quelia>⌋, a pseudo pidgin of German and Russian.#⌋@@@@1@8@@oe@26-8-2013
1000007401080@unknown@formal@none@1@S@⌊#⌊>Runglish>⌋, Russian-English pidgin.@@@@1@3@@oe@26-8-2013
1000007401090@unknown@formal@none@1@S@This word is also used by English speakers to describe the way in which Russians attempt to speak English using Russian morphology and/or syntax.#⌋@@@@1@24@@oe@26-8-2013
1000007401100@unknown@formal@none@1@S@⌊#⌊>Russenorsk>⌋ is an extinct ⌊>pidgin>⌋ language with mostly Russian vocabulary and mostly ⌊>Norwegian>⌋ grammar, used for communication between ⌊>Russians>⌋ and ⌊>Norwegian>⌋ traders in the Pomor trade in ⌊>Finnmark>⌋ and the ⌊>Kola Peninsula>⌋.#⌋•⌋@@@@1@32@@oe@26-8-2013
1000007401110@unknown@formal@none@1@S@⌊=Writing system¦2=⌋@@@@1@2@@oe@26-8-2013
1000007401120@unknown@formal@none@1@S@⌊=Alphabet¦3=⌋@@@@1@1@@oe@26-8-2013
1000007401130@unknown@formal@none@1@S@Russian is written using a modified version of the ⌊>Cyrillic (кириллица)>⌋ alphabet.@@@@1@12@@oe@26-8-2013
1000007401140@unknown@formal@none@1@S@The Russian alphabet consists of 33 letters.@@@@1@7@@oe@26-8-2013
1000007401150@unknown@formal@none@1@S@The following table gives their upper case forms, along with ⌊>IPA>⌋ values for each letter's typical sound:@@@@1@17@@oe@26-8-2013
1000007401160@unknown@formal@none@1@S@Older letters of the Russian alphabet include <ѣ>, which merged to <е> (⌊λ/e/¦/e/¦IPAλ⌋); <і> and <ѵ>, which both merged to <и>(⌊λ/i/¦/i/¦IPAλ⌋); <ѳ>, which merged to <ф> (⌊λ/f/¦/f/¦IPAλ⌋); and <ѧ>, which merged to <я> (⌊λ/ja/¦/ja/¦IPAλ⌋ or ⌊λ/ʲa/¦/ʲa/¦IPAλ⌋).@@@@1@36@@oe@26-8-2013
1000007401170@unknown@formal@none@1@S@While these older letters have been abandoned at one time or another, they may be used in this and related articles.@@@@1@21@@oe@26-8-2013
1000007401180@unknown@formal@none@1@S@The ⌊>yer>⌋s <ъ> and <ь> originally indicated the pronunciation of ⌊/ultra-short/⌋ or ⌊/reduced/⌋ ⌊λ/ŭ/¦/ŭ/¦IPAλ⌋, ⌊λ/ĭ/¦/ĭ/¦IPAλ⌋.@@@@1@15@@oe@26-8-2013
1000007401190@unknown@formal@none@1@S@The Russian alphabet has many systems of ⌊>character encoding>⌋.@@@@1@9@@oe@26-8-2013
1000007401200@unknown@formal@none@1@S@⌊>KOI8-R>⌋ was designed by the government and was intended to serve as the standard encoding.@@@@1@15@@oe@26-8-2013
1000007401210@unknown@formal@none@1@S@This encoding is still used in UNIX-like operating systems.@@@@1@9@@oe@26-8-2013
1000007401220@unknown@formal@none@1@S@Nevertheless, the spread of ⌊>MS-DOS>⌋ and ⌊>Microsoft Windows>⌋ created chaos and ended by establishing different encodings as de-facto standards.@@@@1@19@@oe@26-8-2013
1000007401230@unknown@formal@none@1@S@For communication purposes, a number of conversion applications were developed.@@@@1@10@@oe@26-8-2013
1000007401240@unknown@formal@none@1@S@"⌊>iconv>⌋" is an example that is supported by most versions of ⌊>Linux>⌋, ⌊>Macintosh>⌋ and some other ⌊>operating system>⌋s.@@@@1@18@@oe@26-8-2013
1000007401250@unknown@formal@none@1@S@Most implementations (especially old ones) of the character encoding for the Russian language are aimed at simultaneous use of English and Russian characters only and do not include support for any other language.@@@@1@33@@oe@26-8-2013
1000007401260@unknown@formal@none@1@S@Certain hopes for a unification of the character encoding for the Russian alphabet are related to the ⌊>Unicode standard>⌋, specifically designed for peaceful coexistence of various languages, including even ⌊>dead language>⌋s.@@@@1@31@@oe@26-8-2013
1000007401270@unknown@formal@none@1@S@⌊>Unicode>⌋ also supports the letters of the ⌊>Early Cyrillic alphabet>⌋, which have many similarities with the ⌊>Greek alphabet>⌋.@@@@1@18@@oe@26-8-2013
1000007401280@unknown@formal@none@1@S@⌊=Orthography¦3=⌋@@@@1@1@@oe@26-8-2013
1000007401290@unknown@formal@none@1@S@Russian spelling is reasonably phonemic in practice.@@@@1@7@@oe@26-8-2013
1000007401300@unknown@formal@none@1@S@It is in fact a balance among phonemics, morphology, etymology, and grammar; and, like that of most living languages, has its share of inconsistencies and controversial points.@@@@1@27@@oe@26-8-2013
1000007401310@unknown@formal@none@1@S@A number of rigid ⌊>spelling rule>⌋s introduced between the 1880s and 1910s have been responsible for the latter whilst trying to eliminate the former.@@@@1@24@@oe@26-8-2013
1000007401320@unknown@formal@none@1@S@The current spelling follows the major reform of 1918, and the final codification of 1956.@@@@1@15@@oe@26-8-2013
1000007401330@unknown@formal@none@1@S@An update proposed in the late 1990s has met a hostile reception, and has not been formally adopted.@@@@1@18@@oe@26-8-2013
1000007401340@unknown@formal@none@1@S@The punctuation, originally based on Byzantine Greek, was in the seventeenth and eighteenth centuries reformulated on the French and German models.@@@@1@21@@oe@26-8-2013
1000007401350@unknown@formal@none@1@S@⌊=Sounds¦2=⌋@@@@1@1@@oe@26-8-2013
1000007401360@unknown@formal@none@1@S@The phonological system of Russian is inherited from ⌊>Common Slavonic>⌋, but underwent considerable modification in the early historical period, before being largely settled by about 1400.@@@@1@26@@oe@26-8-2013
1000007401370@unknown@formal@none@1@S@The language possesses five vowels, which are written with different letters depending on whether or not the preceding consonant is ⌊>palatalized>⌋.@@@@1@21@@oe@26-8-2013
1000007401380@unknown@formal@none@1@S@The consonants typically come in plain vs. palatalized pairs, which are traditionally called ⌊/hard/⌋ and ⌊/soft./⌋@@@@1@16@@oe@26-8-2013
1000007401390@unknown@formal@none@1@S@(The ⌊/hard/⌋ consonants are often ⌊>velarized>⌋, especially before back vowels, although in some dialects the velarization is limited to hard ⌊λ/l/¦/l/¦IPAλ⌋).@@@@1@21@@oe@26-8-2013
1000007401400@unknown@formal@none@1@S@The standard language, based on the Moscow dialect, possesses heavy stress and moderate variation in pitch.@@@@1@16@@oe@26-8-2013
1000007401410@unknown@formal@none@1@S@Stressed vowels are somewhat lengthened, while unstressed vowels tend to be reduced to near-close vowels or an unclear ⌊>schwa>⌋.@@@@1@19@@oe@26-8-2013
1000007401420@unknown@formal@none@1@S@(See also: ⌊>vowel reduction in Russian>⌋.)@@@@1@6@@oe@26-8-2013
1000007401430@unknown@formal@none@1@S@The Russian ⌊>syllable>⌋ structure can be quite complex with both initial and final consonant clusters of up to 4 consecutive sounds.@@@@1@21@@oe@26-8-2013
1000007401440@unknown@formal@none@1@S@Using a formula with V standing for the nucleus (vowel) and C for each consonant the structure can be described as follows:@@@@1@22@@oe@26-8-2013
1000007401450@unknown@formal@none@1@S@(C)(C)(C)(C)V(C)(C)(C)(C)@@@@1@1@@oe@26-8-2013
1000007401460@unknown@formal@none@1@S@Clusters of four consonants are not very common, however, especially within a morpheme.@@@@1@13@@oe@26-8-2013
1000007401470@unknown@formal@none@1@S@⌊=Consonants¦3=⌋@@@@1@1@@oe@26-8-2013
1000007401480@unknown@formal@none@1@S@Russian is notable for its distinction based on ⌊>palatalization>⌋ of most of the consonants.@@@@1@14@@oe@26-8-2013
1000007401490@unknown@formal@none@1@S@While ⌊λ/k/, /g/, /x/¦/k/, /g/, /x/¦IPAλ⌋ do have palatalized ⌊>allophone>⌋s ⌊λ[kʲ, gʲ, xʲ]¦[kʲ, gʲ, xʲ]¦IPAλ⌋, only ⌊λ/kʲ/¦/kʲ/¦IPAλ⌋ might be considered a phoneme, though it is marginal and generally not considered distinctive (the only native ⌊>minimal pair>⌋ which argues for ⌊λ/kʲ/¦/kʲ/¦IPAλ⌋ to be a separate phoneme is "это ткёт"/"этот кот").@@@@1@49@@oe@26-8-2013
1000007401500@unknown@formal@none@1@S@Palatalization means that the center of the tongue is raised during and after the articulation of the consonant.@@@@1@18@@oe@26-8-2013
1000007401510@unknown@formal@none@1@S@In the case of ⌊λ/tʲ/ and /dʲ/¦/tʲ/ and /dʲ/¦IPAλ⌋, the tongue is raised enough to produce slight frication (affricate sounds).@@@@1@20@@oe@26-8-2013
1000007401520@unknown@formal@none@1@S@These sounds: ⌊λ/t, d, ʦ, s, z, n and rʲ/¦/t, d, ʦ, s, z, n and rʲ/¦IPAλ⌋ are ⌊>dental>⌋, that is pronounced with the tip of the tongue against the teeth rather than against the ⌊>alveolar ridge>⌋.@@@@1@37@@oe@26-8-2013
1000007401530@unknown@formal@none@1@S@⌊=Grammar¦2=⌋@@@@1@1@@oe@26-8-2013
1000007401540@unknown@formal@none@1@S@Russian has preserved an ⌊>Indo-European>⌋ ⌊>synthetic>⌋-⌊>inflection>⌋al structure, although considerable leveling has taken place.@@@@1@13@@oe@26-8-2013
1000007401550@unknown@formal@none@1@S@Russian grammar encompasses@@@@1@3@@oe@26-8-2013
1000007401560@unknown@formal@none@1@S@⌊•⌊#a highly ⌊>synthetic>⌋ ⌊∗morphology∗⌋#⌋@@@@1@4@@oe@26-8-2013
1000007401570@unknown@formal@none@1@S@⌊#a ⌊∗syntax∗⌋ that, for the literary language, is the conscious fusion of three elements:@@@@1@14@@oe@26-8-2013
1000007401580@unknown@formal@none@1@S@⌊•⌊#a polished ⌊>vernacular>⌋ foundation;#⌋@@@@1@4@@oe@26-8-2013
1000007401590@unknown@formal@none@1@S@⌊#a ⌊>Church Slavonic>⌋ inheritance;#⌋@@@@1@4@@oe@26-8-2013
1000007401600@unknown@formal@none@1@S@⌊#a ⌊>Western Europe>⌋an style.#⌋•⌋#⌋•⌋@@@@1@4@@oe@26-8-2013
1000007401610@unknown@formal@none@1@S@The spoken language has been influenced by the literary one, but continues to preserve characteristic forms.@@@@1@16@@oe@26-8-2013
1000007401620@unknown@formal@none@1@S@The dialects show various non-standard grammatical features, some of which are archaisms or descendants of old forms since discarded by the literary language.@@@@1@23@@oe@26-8-2013
1000007401630@unknown@formal@none@1@S@⌊=Vocabulary¦2=⌋@@@@1@1@@oe@26-8-2013
1000007401640@unknown@formal@none@1@S@See ⌊>History of the Russian language>⌋ for an account of the successive foreign influences on the Russian language.@@@@1@18@@oe@26-8-2013
1000007401650@unknown@formal@none@1@S@The total number of words in Russian is difficult to reckon because of the ability to agglutinate and create manifold compounds, diminutives, etc. (see ⌊>Word Formation>⌋ under ⌊>Russian grammar>⌋).@@@@1@29@@oe@26-8-2013
1000007401660@unknown@formal@none@1@S@The number of listed words or entries in some of the major dictionaries published during the last two centuries, and the total vocabulary of ⌊>Pushkin>⌋ (who is credited with greatly augmenting and codifying literary Russian), are as follows:@@@@1@38@@oe@26-8-2013
1000007401670@unknown@formal@none@1@S@(As a historical aside, ⌊>Dahl>⌋ was, in the second half of the nineteenth century, still insisting that the proper spelling of the adjective ⌊∗русский∗⌋, which was at that time applied uniformly to all the Orthodox Eastern Slavic subjects of the Empire, as well as to its one official language, be spelled ⌊∗руский∗⌋ with one s, in accordance with ancient tradition and what he termed the "spirit of the language".@@@@1@69@@oe@26-8-2013
1000007401680@unknown@formal@none@1@S@He was contradicted by the philologist Grot, who distinctly heard the s lengthened or doubled).@@@@1@15@@oe@26-8-2013
1000007401690@unknown@formal@none@1@S@⌊=Proverbs and sayings¦3=⌋@@@@1@3@@oe@26-8-2013
1000007401700@unknown@formal@none@1@S@The Russian language is replete with many hundreds of proverbs (⌊∗пословица∗⌋ ⌊λ[pɐˈslo.vʲɪ.ʦə]¦[pɐˈslo.vʲɪ.ʦə]¦IPAλ⌋) and sayings (⌊∗поговоркa∗⌋ ⌊λ[pə.gɐˈvo.rkə]¦[pə.gɐˈvo.rkə]¦IPAλ⌋).@@@@1@16@@oe@26-8-2013
1000007401710@unknown@formal@none@1@S@These were already tabulated by the seventeenth century, and collected and studied in the nineteenth and twentieth, with the folk-tales being an especially fertile source.@@@@1@25@@oe@26-8-2013
1000007401720@unknown@formal@none@1@S@⌊=History and examples¦2=⌋@@@@1@3@@oe@26-8-2013
1000007401730@unknown@formal@none@1@S@The history of Russian language may be divided into the following periods.@@@@1@12@@oe@26-8-2013
1000007401740@unknown@formal@none@1@S@⌊•⌊#⌊>Kievan period and feudal breakup>⌋#⌋@@@@1@5@@oe@26-8-2013
1000007401750@unknown@formal@none@1@S@⌊#⌊>The Tatar yoke and the Grand Duchy of Lithuania>⌋#⌋@@@@1@9@@oe@26-8-2013
1000007401760@unknown@formal@none@1@S@⌊#⌊>The Moscovite period (15th–17th centuries)>⌋#⌋@@@@1@5@@oe@26-8-2013
1000007401770@unknown@formal@none@1@S@⌊#⌊>Empire (18th–19th centuries)>⌋#⌋@@@@1@3@@oe@26-8-2013
1000007401780@unknown@formal@none@1@S@⌊#⌊>Soviet period and beyond (20th century)>⌋#⌋•⌋@@@@1@6@@oe@26-8-2013
1000007401790@unknown@formal@none@1@S@Judging by the historical records, by approximately 1000 AD the predominant ethnic group over much of modern European ⌊>Russia>⌋, ⌊>Ukraine>⌋, and ⌊>Belarus>⌋ was the Eastern branch of the ⌊>Slavs>⌋, speaking a closely related group of dialects.@@@@1@36@@oe@26-8-2013
1000007401800@unknown@formal@none@1@S@The political unification of this region into ⌊>Kievan Rus'>⌋ in about 880, from which modern Russia, Ukraine and Belarus trace their origins, established ⌊>Old East Slavic>⌋ as a literary and commercial language.@@@@1@32@@oe@26-8-2013
1000007401810@unknown@formal@none@1@S@It was soon followed by the adoption of ⌊>Christianity>⌋ in 988 and the introduction of the South Slavic ⌊>Old Church Slavonic>⌋ as the liturgical and official language.@@@@1@27@@oe@26-8-2013
1000007401820@unknown@formal@none@1@S@Borrowings and ⌊>calque>⌋s from Byzantine ⌊>Greek>⌋ began to enter the ⌊>Old East Slavic>⌋ and spoken dialects at this time, which in their turn modified the ⌊>Old Church Slavonic>⌋ as well.@@@@1@30@@oe@26-8-2013
1000007401830@unknown@formal@none@1@S@Dialectal differentiation accelerated after the breakup of ⌊>Kievan Rus>⌋ in approximately 1100.@@@@1@12@@oe@26-8-2013
1000007401840@unknown@formal@none@1@S@On the territories of modern ⌊>Belarus>⌋ and ⌊>Ukraine>⌋ emerged ⌊>Ruthenian>⌋ and in modern ⌊>Russia>⌋ ⌊>medieval Russian>⌋.@@@@1@16@@oe@26-8-2013
1000007401850@unknown@formal@none@1@S@They definitely became distinct in 13th century by the time of division of that land between the ⌊>Grand Duchy of Lithuania>⌋ on the west and independent Novgorod Feudal Republic plus small duchies which were vassals of the Tatars on the east.@@@@1@41@@oe@26-8-2013
1000007401860@unknown@formal@none@1@S@The official language in Moscow and Novgorod, and later, in the growing Moscow Rus’, was ⌊>Church Slavonic>⌋ which evolved from ⌊>Old Church Slavonic>⌋ and remained ⌊>the literary language>⌋ until the Petrine age, when its usage shrank drastically to biblical and liturgical texts.@@@@1@42@@oe@26-8-2013
1000007401870@unknown@formal@none@1@S@Russian developed under a strong influence of the Church Slavonic until the close of the seventeenth century; the influence reversed afterwards leading to corruption of liturgical texts.@@@@1@27@@oe@26-8-2013
1000007401880@unknown@formal@none@1@S@The political reforms of ⌊>Peter the Great>⌋ were accompanied by a reform of the alphabet, and achieved their goal of secularization and Westernization.@@@@1@23@@oe@26-8-2013
1000007401890@unknown@formal@none@1@S@Blocks of specialized vocabulary were adopted from the languages of Western Europe.@@@@1@12@@oe@26-8-2013
1000007401900@unknown@formal@none@1@S@By 1800, a significant portion of the gentry spoke ⌊>French>⌋, less often ⌊>German>⌋, on an everyday basis.@@@@1@17@@oe@26-8-2013
1000007401910@unknown@formal@none@1@S@Many Russian novels of the 19th century, e.g. Lev Tolstoy’s "War and Peace", contain entire paragraphs and even pages in French with no translation given, with an assumption that educated readers won't need one.@@@@1@34@@oe@26-8-2013
1000007401920@unknown@formal@none@1@S@The modern literary language is usually considered to date from the time of ⌊>Aleksandr Pushkin>⌋ in the first third of the nineteenth century.@@@@1@23@@oe@26-8-2013
1000007401930@unknown@formal@none@1@S@Pushkin revolutionized Russian literature by rejecting archaic grammar and vocabulary (so called "высокий стиль" — "high style") in favor of grammar and vocabulary found in the spoken language of the time.@@@@1@31@@oe@26-8-2013
1000007401940@unknown@formal@none@1@S@Even modern readers of younger age may only experience slight difficulties understanding some words in Pushkin’s texts, since only few words used by Pushkin became archaic or changed meaning.@@@@1@29@@oe@26-8-2013
1000007401950@unknown@formal@none@1@S@On the other hand, many expressions used by Russian writers of the early 19th century, in particular Pushkin, ⌊>Lermontov>⌋, ⌊>Gogol>⌋, Griboiädov, became proverbs or sayings which can be frequently found even in the modern Russian colloquial speech.@@@@1@37@@oe@26-8-2013
1000007401960@unknown@formal@none@1@S@The political upheavals of the early twentieth century and the wholesale changes of political ideology gave written Russian its modern appearance after the spelling reform of 1918.@@@@1@27@@oe@26-8-2013
1000007401970@unknown@formal@none@1@S@Political circumstances and Soviet accomplishments in military, scientific, and technological matters (especially cosmonautics), gave Russian a world-wide prestige, especially during the middle third of the twentieth century.@@@@1@27@@oe@26-8-2013
1000007500010@unknown@formal@none@1@S@⌊δSYSTRANδ⌋@@@@1@1@@oe@26-8-2013
1000007500020@unknown@formal@none@1@S@⌊∗SYSTRAN∗⌋, founded by Dr. ⌊>Peter Toma>⌋ in ⌊>1968>⌋, is one of the oldest ⌊>machine translation>⌋ companies.@@@@1@16@@oe@26-8-2013
1000007500030@unknown@formal@none@1@S@SYSTRAN has done extensive work for the ⌊>United States Department of Defense>⌋ and the ⌊>European Commission>⌋.@@@@1@16@@oe@26-8-2013
1000007500040@unknown@formal@none@1@S@SYSTRAN provides the technology for ⌊>Yahoo!>⌋ and ⌊>AltaVista>⌋'s (⌊>Babel Fish>⌋) among others, but use of it was ended (circa 2007) for all of the language combinations offered by ⌊>Google>⌋'s ⌊>language tools>⌋.@@@@1@31@@oe@26-8-2013
1000007500050@unknown@formal@none@1@S@Commercial versions of SYSTRAN operate with operating systems ⌊>Microsoft Windows>⌋ (including ⌊>Windows Mobile>⌋), ⌊>Linux>⌋ and ⌊>Solaris>⌋.@@@@1@16@@oe@26-8-2013
1000007500060@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000007500070@unknown@formal@none@1@S@With its origin in the ⌊>Georgetown>⌋ machine translation effort, SYSTRAN was one of the few machine translation systems to survive the major decrease of funding after the ⌊>ALPAC Report>⌋ of the mid-1960's.@@@@1@32@@oe@26-8-2013
1000007500080@unknown@formal@none@1@S@The company was established in ⌊>La Jolla>⌋, ⌊>California>⌋ to work on translation of Russian to English text for the ⌊>United States Air Force>⌋ during the "⌊>Cold War>⌋".@@@@1@27@@oe@26-8-2013
1000007500090@unknown@formal@none@1@S@Large numbers of Russian scientific and technical documents were translated using SYSTRAN under the auspices of the USAF Foreign Technology Division (later the National Air and Space Intelligence Center) at ⌊>Wright-Patterson Air Force Base>⌋, Ohio.@@@@1@35@@oe@26-8-2013
1000007500100@unknown@formal@none@1@S@The quality of the translations, although only approximate, was usually adequate for understanding content.@@@@1@14@@oe@26-8-2013
1000007500110@unknown@formal@none@1@S@The company was sold during 1986 to the Gachot family, based in ⌊>Paris>⌋, ⌊>France>⌋, and is now traded publicly by the French stock exchange.@@@@1@24@@oe@26-8-2013
1000007500120@unknown@formal@none@1@S@It has a main office at the ⌊>Grande Arche>⌋ in ⌊>La Defense>⌋ and maintains a secondary office in ⌊>La Jolla, San Diego, California>⌋.@@@@1@23@@oe@26-8-2013
1000007600010@unknown@formal@none@1@S@⌊δSemanticsδ⌋@@@@1@1@@oe@26-8-2013
1000007600020@unknown@formal@none@1@S@⌊∗Semantics∗⌋ is the study of meaning in communication.@@@@1@8@@oe@26-8-2013
1000007600030@unknown@formal@none@1@S@The word derives from ⌊>Greek>⌋ ⌊/σημαντικός/⌋ (⌊/semantikos/⌋), "significant", from ⌊/σημαίνω/⌋ (⌊/semaino/⌋), "to signify, to indicate" and that from ⌊/σήμα/⌋ (⌊/sema/⌋), "sign, mark, token".@@@@1@23@@oe@26-8-2013
1000007600040@unknown@formal@none@1@S@In ⌊>linguistics>⌋ it is the study of interpretation of signs as used by ⌊>agent>⌋s or ⌊>communities>⌋ within particular circumstances and contexts.@@@@1@21@@oe@26-8-2013
1000007600050@unknown@formal@none@1@S@It has related meanings in several other fields.@@@@1@8@@oe@26-8-2013
1000007600060@unknown@formal@none@1@S@Semanticists differ on what constitutes ⌊>meaning>⌋ in an expression.@@@@1@9@@oe@26-8-2013
1000007600070@unknown@formal@none@1@S@For example, in the sentence, "John loves a bagel", the word ⌊/bagel/⌋ may refer to the object itself, which is its ⌊/literal/⌋ meaning or ⌊/⌊>denotation>⌋/⌋, but it may also refer to many other figurative associations, such as how it meets John's hunger, etc., which may be its ⌊/⌊>connotation>⌋/⌋.@@@@1@48@@oe@26-8-2013
1000007600080@unknown@formal@none@1@S@Traditionally, the ⌊>formal semantic>⌋ view restricts semantics to its literal meaning, and relegates all figurative associations to ⌊>pragmatics>⌋, but this distinction is increasingly difficult to defend.@@@@1@26@@oe@26-8-2013
1000007600090@unknown@formal@none@1@S@The degree to which a theorist subscribes to the literal-figurative distinction decreases as one moves from the ⌊>formal semantic>⌋, ⌊>semiotic>⌋, ⌊>pragmatic>⌋, to the ⌊>cognitive semantic>⌋ traditions.@@@@1@26@@oe@26-8-2013
1000007600100@unknown@formal@none@1@S@The word ⌊/semantic/⌋ in its modern sense is considered to have first appeared in ⌊>French>⌋ as ⌊/sémantique/⌋ in ⌊>Michel Bréal>⌋'s 1897 book, ⌊/Essai de sémantique'./⌋@@@@1@25@@oe@26-8-2013
1000007600110@unknown@formal@none@1@S@In ⌊>International Scientific Vocabulary>⌋ semantics is also called ⌊/⌊>semasiology>⌋/⌋.@@@@1@9@@oe@26-8-2013
1000007600120@unknown@formal@none@1@S@The discipline of Semantics is distinct from ⌊>Alfred Korzybski's General Semantics>⌋, which is a system for looking at non-immediate, or abstract meanings.@@@@1@22@@oe@26-8-2013
1000007600130@unknown@formal@none@1@S@⌊=Linguistics¦2=⌋@@@@1@1@@oe@26-8-2013
1000007600140@unknown@formal@none@1@S@In ⌊>linguistics>⌋, ⌊∗semantics∗⌋ is the subfield that is devoted to the study of meaning, as inherent at the levels of words, phrases, sentences, and even larger units of ⌊>discourse>⌋ (referred to as ⌊/texts/⌋).@@@@1@33@@oe@26-8-2013
1000007600150@unknown@formal@none@1@S@The basic area of study is the meaning of ⌊>sign>⌋s, and the study of relations between different linguistic units: ⌊>homonym>⌋y, ⌊>synonym>⌋y, ⌊>antonym>⌋y, ⌊>polysemy>⌋, ⌊>paronyms>⌋, ⌊>hypernym>⌋y, ⌊>hyponym>⌋y, ⌊>meronymy>⌋, ⌊>metonymy>⌋, ⌊>holonymy>⌋, ⌊>exocentric>⌋ity / ⌊>endocentric>⌋ity, linguistic ⌊>compounds>⌋.@@@@1@34@@oe@26-8-2013
1000007600160@unknown@formal@none@1@S@A key concern is how meaning attaches to larger chunks of text, possibly as a result of the composition from smaller units of meaning.@@@@1@24@@oe@26-8-2013
1000007600170@unknown@formal@none@1@S@Traditionally, semantics has included the study of connotative ⌊/⌊>sense>⌋/⌋ and denotative ⌊/⌊>reference>⌋/⌋, ⌊>truth condition>⌋s, ⌊>argument structure>⌋, ⌊>thematic role>⌋s, ⌊>discourse analysis>⌋, and the linkage of all of these to syntax.@@@@1@29@@oe@26-8-2013
1000007600180@unknown@formal@none@1@S@⌊>Formal semanticists>⌋ are concerned with the modeling of meaning in terms of the semantics of logic.@@@@1@16@@oe@26-8-2013
1000007600190@unknown@formal@none@1@S@Thus the sentence ⌊/John loves a bagel/⌋ above can be broken down into its constituents (signs), of which the unit ⌊/loves/⌋ may serve as both syntactic and semantic ⌊>head>⌋.@@@@1@29@@oe@26-8-2013
1000007600200@unknown@formal@none@1@S@In the late 1960s, ⌊>Richard Montague>⌋ proposed a system for defining semantic entries in the lexicon in terms of ⌊>lambda calculus>⌋.@@@@1@21@@oe@26-8-2013
1000007600210@unknown@formal@none@1@S@Thus, the syntactic ⌊>parse>⌋ of the sentence above would now indicate ⌊/loves/⌋ as the head, and its entry in the lexicon would point to the arguments as the agent, ⌊/John/⌋, and the object, ⌊/bagel/⌋, with a special role for the article "a" (which Montague called a quantifier).@@@@1@47@@oe@26-8-2013
1000007600220@unknown@formal@none@1@S@This resulted in the sentence being associated with the logical predicate ⌊/loves (John, bagel)/⌋, thus linking semantics to ⌊>categorial grammar>⌋ models of ⌊>syntax>⌋.@@@@1@23@@oe@26-8-2013
1000007600230@unknown@formal@none@1@S@The logical predicate thus obtained would be elaborated further, e.g. using truth theory models, which ultimately relate meanings to a set of ⌊>Tarski>⌋ian universals, which may lie outside the logic.@@@@1@30@@oe@26-8-2013
1000007600240@unknown@formal@none@1@S@The notion of such meaning atoms or primitives are basic to the ⌊>language of thought>⌋ hypothesis from the 70s.@@@@1@19@@oe@26-8-2013
1000007600250@unknown@formal@none@1@S@Despite its elegance, ⌊>Montague grammar>⌋ was limited by the context-dependent variability in word sense, and led to several attempts at incorporating context, such as :@@@@1@25@@oe@26-8-2013
1000007600260@unknown@formal@none@1@S@⌊•⌊#⌊>situation semantics>⌋ ('80s): Truth-values are incomplete, they get assigned based on context#⌋@@@@1@12@@oe@26-8-2013
1000007600270@unknown@formal@none@1@S@⌊#⌊>generative lexicon>⌋ ('90s): categories (types) are incomplete, and get assigned based on context#⌋•⌋@@@@1@13@@oe@26-8-2013
1000007600280@unknown@formal@none@1@S@⌊=The dynamic turn in semantics¦3=⌋@@@@1@5@@oe@26-8-2013
1000007600290@unknown@formal@none@1@S@In the ⌊>Chomskian>⌋ tradition in linguistics there was no mechanism for the learning of semantic relations, and the ⌊>nativist>⌋ view considered all semantic notions as inborn.@@@@1@26@@oe@26-8-2013
1000007600300@unknown@formal@none@1@S@Thus, even novel concepts were proposed to have been dormant in some sense.@@@@1@13@@oe@26-8-2013
1000007600310@unknown@formal@none@1@S@This traditional view was also unable to address many issues such as ⌊>metaphor>⌋ or associative meanings, and ⌊>semantic change>⌋, where meanings within a linguistic community change over time, and ⌊>qualia>⌋ or subjective experience.@@@@1@33@@oe@26-8-2013
1000007600320@unknown@formal@none@1@S@Another issue not addressed by the nativist model was how perceptual cues are combined in thought, e.g. in ⌊>mental rotation>⌋.@@@@1@20@@oe@26-8-2013
1000007600330@unknown@formal@none@1@S@This traditional view of semantics, as an innate finite meaning inherent in a ⌊>lexical unit>⌋ that can be composed to generate meanings for larger chunks of discourse, is now being fiercely debated in the emerging domain of ⌊>cognitive linguistics>⌋ and also in the non-⌊>Fodorian>⌋ camp in ⌊>Philosophy of Language>⌋.@@@@1@49@@oe@26-8-2013
1000007600340@unknown@formal@none@1@S@The challenge is motivated by@@@@1@5@@oe@26-8-2013
1000007600350@unknown@formal@none@1@S@⌊•⌊#factors internal to language, such as the problem of resolving ⌊>indexical>⌋ or ⌊>anaphora>⌋ (e.g. ⌊/this x/⌋, ⌊/him/⌋, ⌊/last week/⌋).@@@@1@19@@oe@26-8-2013
1000007600360@unknown@formal@none@1@S@In these situations "context" serves as the input, but the interpreted utterance also modifies the context, so it is also the output.@@@@1@22@@oe@26-8-2013
1000007600370@unknown@formal@none@1@S@Thus, the interpretation is necessarily dynamic and the meaning of sentences is viewed as context-change potentials instead of ⌊>propositions>⌋.#⌋@@@@1@19@@oe@26-8-2013
1000007600380@unknown@formal@none@1@S@⌊#factors external to language, i.e. language is not a set of labels stuck on things, but "a toolbox, the importance of whose elements lie in the way they function rather than their attachments to things."@@@@1@35@@oe@26-8-2013
1000007600390@unknown@formal@none@1@S@This view reflects the position of the later ⌊>Wittgenstein>⌋ and his famous ⌊/game/⌋ example, and is related to the positions of ⌊>Quine>⌋, ⌊>Davidson>⌋, and others.#⌋•⌋@@@@1@25@@oe@26-8-2013
1000007600400@unknown@formal@none@1@S@A concrete example of the latter phenomenon is semantic ⌊>underspecification>⌋ — meanings are not complete without some elements of context.@@@@1@20@@oe@26-8-2013
1000007600410@unknown@formal@none@1@S@To take an example of a single word, "red", its meaning in a phrase such as ⌊/red book/⌋ is similar to many other usages, and can be viewed as compositional.@@@@1@30@@oe@26-8-2013
1000007600420@unknown@formal@none@1@S@However, the colours implied in phrases such as "red wine" (very dark), and "red hair" (coppery), or "red soil", or "red skin" are very different.@@@@1@25@@oe@26-8-2013
1000007600430@unknown@formal@none@1@S@Indeed, these colours by themselves would not be called "red" by native speakers.@@@@1@13@@oe@26-8-2013
1000007600440@unknown@formal@none@1@S@These instances are contrastive, so "red wine" is so called only in comparison with the other kind of wine (which also is not "white" for the same reasons).@@@@1@28@@oe@26-8-2013
1000007600450@unknown@formal@none@1@S@This view goes back to ⌊>de Saussure>⌋:@@@@1@7@@oe@26-8-2013
1000007600460@unknown@formal@none@1@S@⌊⇥Each of a set of synonyms like ⌊/redouter/⌋ ('to dread'), ⌊/craindre/⌋ ('to fear'), ⌊/avoir peur/⌋ ('to be afraid') has its particular value only because they stand in contrast with one another.@@@@1@31@@oe@26-8-2013
1000007600470@unknown@formal@none@1@S@No word has a value that can be identified independently of what else is in its vicinity.⇥⌋@@@@1@17@@oe@26-8-2013
1000007600480@unknown@formal@none@1@S@and may go back to earlier ⌊>India>⌋n views on language, especially the ⌊>Nyaya>⌋ view of words as ⌊>indicators>⌋ and not carriers of meaning.@@@@1@23@@oe@26-8-2013
1000007600490@unknown@formal@none@1@S@An attempt to defend a system based on propositional meaning for semantic underspecification can be found in the ⌊>Generative Lexicon>⌋ model of ⌊>James Pustejovsky>⌋, who extends contextual operations (based on type shifting) into the lexicon.@@@@1@35@@oe@26-8-2013
1000007600500@unknown@formal@none@1@S@Thus meanings are generated on the fly based on finite context.@@@@1@11@@oe@26-8-2013
1000007600510@unknown@formal@none@1@S@⌊=Prototype theory¦3=⌋@@@@1@2@@oe@26-8-2013
1000007600520@unknown@formal@none@1@S@Another set of concepts related to fuzziness in semantics is based on ⌊>prototype>⌋s.@@@@1@13@@oe@26-8-2013
1000007600530@unknown@formal@none@1@S@The work of ⌊>Eleanor Rosch>⌋ and ⌊>George Lakoff>⌋ in the 1970s led to a view that natural categories are not characterizable in terms of necessary and sufficient conditions, but are graded (fuzzy at their boundaries) and inconsistent as to the status of their constituent members.@@@@1@45@@oe@26-8-2013
1000007600540@unknown@formal@none@1@S@Systems of categories are not objectively "out there" in the world but are rooted in people's experience.@@@@1@17@@oe@26-8-2013
1000007600550@unknown@formal@none@1@S@These categories evolve as ⌊>learned>⌋ concepts of the world — meaning is not an objective truth, but a subjective construct, learned from experience, and language arises out of the "grounding of our conceptual systems in shared ⌊>embodiment>⌋ and bodily experience".@@@@1@40@@oe@26-8-2013
1000007600560@unknown@formal@none@1@S@A corollary of this is that the conceptual categories (i.e. the lexicon) will not be identical for different cultures, or indeed, for every individual in the same culture.@@@@1@28@@oe@26-8-2013
1000007600570@unknown@formal@none@1@S@This leads to another debate (see the ⌊>Whorf-Sapir hypothesis>⌋ or ⌊>Eskimo words for snow>⌋).@@@@1@14@@oe@26-8-2013
1000007600580@unknown@formal@none@1@S@⌊=Computer science¦2=⌋@@@@1@2@@oe@26-8-2013
1000007600590@unknown@formal@none@1@S@In ⌊>computer science>⌋, where it is considered as an application of ⌊>mathematical logic>⌋, semantics reflects the meaning of programs or functions.@@@@1@21@@oe@26-8-2013
1000007600600@unknown@formal@none@1@S@In this regard, semantics permits programs to be separated into their syntactical part (grammatical structure) and their semantic part (meaning).@@@@1@20@@oe@26-8-2013
1000007600610@unknown@formal@none@1@S@For instance, the following statements use different syntaxes (languages), but result in the same semantic:@@@@1@15@@oe@26-8-2013
1000007600620@unknown@formal@none@1@S@⌊•⌊#x += y; (⌊>C>⌋, ⌊>Java>⌋, etc.)#⌋@@@@1@6@@oe@26-8-2013
1000007600630@unknown@formal@none@1@S@⌊#x := x + y; (⌊>Pascal>⌋)#⌋@@@@1@6@@oe@26-8-2013
1000007600640@unknown@formal@none@1@S@⌊#Let x = x + y; (early ⌊>BASIC>⌋)#⌋@@@@1@8@@oe@26-8-2013
1000007600650@unknown@formal@none@1@S@⌊#x = x + y (most BASIC dialects, ⌊>Fortran>⌋)#⌋•⌋@@@@1@9@@oe@26-8-2013
1000007600660@unknown@formal@none@1@S@Generally these operations would all perform an arithmetical addition of 'y' to 'x' and store the result in a variable 'x'.@@@@1@21@@oe@26-8-2013
1000007600670@unknown@formal@none@1@S@Semantics for computer applications falls into three categories:@@@@1@8@@oe@26-8-2013
1000007600680@unknown@formal@none@1@S@⌊•⌊#⌊>Operational semantics>⌋: The meaning of a construct is specified by the computation it induces when it is executed on a machine.@@@@1@21@@oe@26-8-2013
1000007600690@unknown@formal@none@1@S@In particular, it is of interest ⌊/how/⌋ the effect of a computation is produced.#⌋•⌋@@@@1@14@@oe@26-8-2013
1000007600700@unknown@formal@none@1@S@⌊•⌊#⌊>Denotational semantics>⌋: Meanings are modelled by mathematical objects that represent the effect of executing the constructs.@@@@1@16@@oe@26-8-2013
1000007600710@unknown@formal@none@1@S@Thus ⌊/only/⌋ the effect is of interest, not how it is obtained.#⌋•⌋@@@@1@12@@oe@26-8-2013
1000007600720@unknown@formal@none@1@S@⌊•⌊#⌊>Axiomatic semantics>⌋: Specific properties of the effect of executing the constructs as expressed as ⌊/assertions/⌋.@@@@1@15@@oe@26-8-2013
1000007600730@unknown@formal@none@1@S@Thus there may be aspects of the executions that are ignored.#⌋•⌋@@@@1@11@@oe@26-8-2013
1000007600740@unknown@formal@none@1@S@The ⌊∗⌊>Semantic Web>⌋∗⌋ refers to the extension of the ⌊>World Wide Web>⌋ through the embedding of additional semantic ⌊>metadata>⌋; s.a.@@@@1@20@@oe@26-8-2013
1000007600750@unknown@formal@none@1@S@⌊>Web Ontology Language>⌋ (OWL).@@@@1@4@@oe@26-8-2013
1000007600760@unknown@formal@none@1@S@⌊=Psychology¦2=⌋@@@@1@1@@oe@26-8-2013
1000007600770@unknown@formal@none@1@S@In ⌊>psychology>⌋, ⌊/⌊>semantic memory>⌋/⌋ is memory for meaning, in other words, the aspect of memory that preserves only the ⌊/gist/⌋, the general significance, of remembered experience, while ⌊>episodic memory>⌋ is memory for the ephemeral details, the individual features, or the unique particulars of experience.@@@@1@44@@oe@26-8-2013
1000007600780@unknown@formal@none@1@S@Word meaning is measured by the company they keep; the relationships among words themselves in a ⌊>semantic network>⌋.@@@@1@18@@oe@26-8-2013
1000007600790@unknown@formal@none@1@S@In a network created by people analyzing their understanding of the word (such as ⌊>Wordnet>⌋) the links and decomposition structures of the network are few in number and kind; and include "part of", "kind of", and similar links.@@@@1@38@@oe@26-8-2013
1000007600800@unknown@formal@none@1@S@In automated ⌊>ontologies>⌋ the links are computed vectors without explicit meaning.@@@@1@11@@oe@26-8-2013
1000007600810@unknown@formal@none@1@S@Various automated technologies are being developed to compute the meaning of words: ⌊>latent semantic indexing>⌋ and ⌊>support vector machines>⌋ as well as ⌊>natural language processing>⌋, ⌊>neural networks>⌋ and ⌊>predicate calculus>⌋ techniques.@@@@1@31@@oe@26-8-2013
1000007600820@unknown@formal@none@1@S@Semantics has been reported to drive the course of psychotherapeutic interventions.@@@@1@11@@oe@26-8-2013
1000007600830@unknown@formal@none@1@S@Language structure can determine the treatment approach to drug-abusing patients. .@@@@1@11@@oe@26-8-2013
1000007600840@unknown@formal@none@1@S@While working in Europe for the US Information Agency, American psychiatrist, Dr. A. James Giannini reported semantic differences in medical approaches to addiction treatment..@@@@1@24@@oe@26-8-2013
1000007600850@unknown@formal@none@1@S@English speaking countries used the term "drug dependence" to describe a rather passive pathology in their patients.@@@@1@17@@oe@26-8-2013
1000007600860@unknown@formal@none@1@S@As a result the physician's role was more active.@@@@1@9@@oe@26-8-2013
1000007600870@unknown@formal@none@1@S@Southern European countries such as Italy and Yugoslavia utilized the concept of "tossicomania" (i.e. toxic mania) to describe a more acive rather than passive role of the addict.@@@@1@28@@oe@26-8-2013
1000007600880@unknown@formal@none@1@S@As a result the treating physician's role shifted to that of a more passive guide than that of an active interventionist. .@@@@1@22@@oe@26-8-2013
1000007700010@unknown@formal@none@1@S@⌊δSentence (linguistics)δ⌋@@@@1@2@@oe@26-8-2013
1000007700020@unknown@formal@none@1@S@In ⌊>linguistics>⌋, a ⌊∗sentence∗⌋ is a grammatical unit of one or more words, bearing minimal syntactic relation to the words that precede or follow it, often preceded and followed in speech by pauses, having one of a small number of characteristic intonation patterns, and typically expressing an independent statement, question, request, command, etc.@@@@1@53@@oe@26-8-2013
1000007700030@unknown@formal@none@1@S@Sentences are generally characterized in most languages by the presence of a ⌊>finite verb>⌋, e.g. "⌊>The quick brown fox jumps over the lazy dog>⌋".@@@@1@24@@oe@26-8-2013
1000007700040@unknown@formal@none@1@S@⌊=Components of a sentence¦2=⌋@@@@1@4@@oe@26-8-2013
1000007700050@unknown@formal@none@1@S@A simple ⌊/complete sentence/⌋ consists of a ⌊/⌊>subject>⌋/⌋ and a ⌊/⌊>predicate>⌋/⌋.@@@@1@11@@oe@26-8-2013
1000007700060@unknown@formal@none@1@S@The subject is typically a ⌊>noun phrase>⌋, though other kinds of phrases (such as ⌊>gerund>⌋ phrases) work as well, and some languages allow subjects to be omitted.@@@@1@27@@oe@26-8-2013
1000007700070@unknown@formal@none@1@S@The predicate is a finite ⌊>verb phrase>⌋: it's a finite verb together with zero or more ⌊>objects>⌋, zero or more ⌊>complements>⌋, and zero or more ⌊>adverbial>⌋s.@@@@1@26@@oe@26-8-2013
1000007700080@unknown@formal@none@1@S@See also ⌊>copula>⌋ for the consequences of this verb on the theory of sentence structure.@@@@1@15@@oe@26-8-2013
1000007700090@unknown@formal@none@1@S@⌊=Clauses¦3=⌋@@@@1@1@@oe@26-8-2013
1000007700100@unknown@formal@none@1@S@A ⌊>clause>⌋ consists of a subject and a verb.@@@@1@9@@oe@26-8-2013
1000007700110@unknown@formal@none@1@S@There are two types of clauses: independent and subordinate (dependent).@@@@1@10@@oe@26-8-2013
1000007700120@unknown@formal@none@1@S@An independent clause consists of a subject verb and also demonstrates a complete thought: for example, "I am sad."@@@@1@19@@oe@26-8-2013
1000007700130@unknown@formal@none@1@S@A subordinate clause consists of a subject and a verb, but demonstrates an incomplete thought: for example, "Because I had to move."@@@@1@22@@oe@26-8-2013
1000007700140@unknown@formal@none@1@S@⌊=Classification¦2=⌋@@@@1@1@@oe@26-8-2013
1000007700150@unknown@formal@none@1@S@⌊=By structure¦3=⌋@@@@1@2@@oe@26-8-2013
1000007700160@unknown@formal@none@1@S@One traditional scheme for classifying ⌊>English>⌋ sentences is by the number and types of ⌊>finite>⌋ ⌊>clause>⌋s:@@@@1@16@@oe@26-8-2013
1000007700170@unknown@formal@none@1@S@⌊•⌊#A ⌊/⌊>simple sentence>⌋/⌋ consists of a single ⌊>independent clause>⌋ with no ⌊>dependent clause>⌋s.#⌋@@@@1@13@@oe@26-8-2013
1000007700180@unknown@formal@none@1@S@⌊#A ⌊/⌊>compound sentence>⌋/⌋ consists of multiple independent clauses with no dependent clauses.@@@@1@12@@oe@26-8-2013
1000007700190@unknown@formal@none@1@S@These clauses are joined together using ⌊>conjunctions>⌋, ⌊>punctuation>⌋, or both.#⌋@@@@1@10@@oe@26-8-2013
1000007700200@unknown@formal@none@1@S@⌊#A ⌊/⌊>complex sentence>⌋/⌋ consists of one or more independent clauses with at least one dependent clause.#⌋@@@@1@16@@oe@26-8-2013
1000007700210@unknown@formal@none@1@S@⌊#A ⌊/⌊>complex-compound sentence>⌋/⌋ (or ⌊/compound-complex sentence/⌋) consists of multiple independent clauses, at least one of which has at least one dependent clause.#⌋•⌋@@@@1@22@@oe@26-8-2013
1000007700220@unknown@formal@none@1@S@⌊=By purpose¦3=⌋@@@@1@2@@oe@26-8-2013
1000007700230@unknown@formal@none@1@S@Sentences can also be classified based on their purpose:@@@@1@9@@oe@26-8-2013
1000007700240@unknown@formal@none@1@S@⌊•⌊#A ⌊/declarative sentence/⌋ or ⌊/declaration/⌋, the most common type, commonly makes a statement: ⌊/I am going home./⌋#⌋@@@@1@17@@oe@26-8-2013
1000007700250@unknown@formal@none@1@S@⌊#A ⌊/negative sentence/⌋ or ⌊/⌊>negation>⌋/⌋ denies that a statement is true: ⌊/I am not going home./⌋#⌋@@@@1@16@@oe@26-8-2013
1000007700260@unknown@formal@none@1@S@⌊#An ⌊/interrogative sentence/⌋ or ⌊/⌊>question>⌋/⌋ is commonly used to request information — ⌊/When are you going to work?/⌋ — but sometimes not; ⌊/see/⌋ ⌊>rhetorical question>⌋.#⌋@@@@1@25@@oe@26-8-2013
1000007700270@unknown@formal@none@1@S@⌊#An ⌊/exclamatory sentence/⌋ or ⌊/⌊>exclamation>⌋/⌋ is generally a more emphatic form of statement: ⌊/What a wonderful day this is!/⌋#⌋•⌋@@@@1@19@@oe@26-8-2013
1000007700280@unknown@formal@none@1@S@⌊=Major and minor sentences¦3=⌋@@@@1@4@@oe@26-8-2013
1000007700290@unknown@formal@none@1@S@A major sentence is a ⌊/regular/⌋ sentence; it has a ⌊>subject>⌋ and a ⌊>predicate>⌋.@@@@1@14@@oe@26-8-2013
1000007700300@unknown@formal@none@1@S@For example: ⌊/I have a ball./⌋@@@@1@6@@oe@26-8-2013
1000007700310@unknown@formal@none@1@S@In this sentence one can change the persons: ⌊/We have a ball./⌋@@@@1@12@@oe@26-8-2013
1000007700320@unknown@formal@none@1@S@However, a minor sentence is an irregular type of sentence.@@@@1@10@@oe@26-8-2013
1000007700330@unknown@formal@none@1@S@It does not contain a finite verb.@@@@1@7@@oe@26-8-2013
1000007700340@unknown@formal@none@1@S@For example, "Mary!"@@@@1@3@@oe@26-8-2013
1000007700350@unknown@formal@none@1@S@"Yes."@@@@1@1@@oe@26-8-2013
1000007700360@unknown@formal@none@1@S@"Coffee." etc.@@@@1@2@@oe@26-8-2013
1000007700370@unknown@formal@none@1@S@Other examples of minor sentences are headings (e.g. the heading of this entry), stereotyped expressions (⌊/Hello!/⌋), emotional expressions (⌊/Wow!/⌋), proverbs, etc.@@@@1@21@@oe@26-8-2013
1000007700380@unknown@formal@none@1@S@This can also include sentences which do not contain verbs (e.g. ⌊/The more, the merrier./⌋) in order to intensify the meaning around the nouns (normally found in poetry and catchphrases) by Judee N..@@@@1@33@@oe@26-8-2013