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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 the older version of Stanford NER tagger that uses a
 * Conditional Markov Model (a.k.a., Maximum Entropy Markov Model or MEMM)
 * designed for Named Entity Recognition, and various suppport code.
 * For newer version of the Stanford NER tagger that is based on Conditional
 * Random Fields (CRF) models, please see edu.stanford.nlp.ie.crf.CRFClassifier.
 * The original version was written for the CoNLL 2003 Shared Task competition.
 * The design was considerably generalized, and many more features added for the
 * BioCreative 2003 evaluation.  For basic NER tagging, you should use
 * CMMClassifier.  (Earlier versions still exist as
 * NGramStringClassifier and BioNGramStringClassifier.)
 * The rest of the code in this directory is various remnants of our
 * BioCreative system and various support utilities for NER or CMMClassifier.
 * 
 * 
 */
package edu.stanford.nlp.ie.ner;