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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.
Fast and robust NLP components implemented in Java.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
Module that defines the ContentPart defining the NLP processing metadata.
Fast and robust NLP components implemented in Java.

ClearTK wrapper for OpenNLP tools
This wraps the ClearNLP dependency parser and semantic role labeler into a UIMA friendly annotator