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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

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/**
 * This package contains a library, TokensRegex, for matching regular expressions over
 * tokens.  TokensRegex is incorporated into the
 * {@link edu.stanford.nlp.pipeline.TokensRegexAnnotator},
 * the {@link edu.stanford.nlp.pipeline.TokensRegexNERAnnotator},
 * and the SUTime functionality in {@link edu.stanford.nlp.pipeline.NERCombinerAnnotator}.
 *
 * Rules for extracting expression using TokensRegex
 *
 * TokensRegex provides a language for specifying rules to extract expressions over a token sequence.
 * {@link edu.stanford.nlp.ling.tokensregex.CoreMapExpressionExtractor} and
 * {@link edu.stanford.nlp.ling.tokensregex.SequenceMatchRules} describes
 * the language and how the extraction rules are created.
 *
 * Core classes for token sequence matching using TokensRegex
 *
 * At the core of TokensRegex are the
 * {@link edu.stanford.nlp.ling.tokensregex.TokenSequenceMatcher} and
 * {@link edu.stanford.nlp.ling.tokensregex.TokenSequencePattern} classes which
 * can be used to match patterns over a sequences of tokens.
 * The usage is designed to follow the paradigm of the Java regular expression library
 * {@code java.util.regex}.  The usage is similar except that matches are done
 * over {@code List<CoreMap>} instead of over {@code String}.
 * 
 * Example:
 *  {@code List tokens = ...;
 * TokenSequencePattern pattern = TokenSequencePattern.compile(...);
 * TokenSequenceMatcher matcher = pattern.getMatcher(tokens);
 * }
 * The classes {@link edu.stanford.nlp.ling.tokensregex.SequenceMatcher} and
 * {@link edu.stanford.nlp.ling.tokensregex.SequencePattern} can be used to build
 * classes for recognizing regular expressions over sequences of arbitrary types.
 *
 * Utility classes
 *
 * TokensRegex also offers a group of utility classes.
 * 
 * {@link edu.stanford.nlp.ling.tokensregex.MultiPatternMatcher} provides utility functions for
 * finding expressions with multiple patterns.
 * For instance, using {@link edu.stanford.nlp.ling.tokensregex.MultiPatternMatcher#findNonOverlapping}
 * you can find all nonoverlapping subsequences for a given set of patterns.
 * 
 * To find character offsets of multiple word expressions in a {@code String},
 * you can also use
 * {@link edu.stanford.nlp.ling.tokensregex.MultiWordStringMatcher#findTargetStringOffsets}.
 * 
 *
 * @author Angel Chang ([email protected])
 */
package edu.stanford.nlp.ling.tokensregex;