Stanford CoreNLP Toolkit

(Redirected from Stanford Core NLP)
Jump to navigation Jump to search

A Stanford CoreNLP Toolkit is an NLP toolkit developed by The Stanford Natural Language Processing Group.




    • QUOTE: Stanford CoreNLP provides a set of natural language analysis tools which can take raw 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, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, etc. Stanford CoreNLP is an integrated framework. Its goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. Starting from plain text, you can run all the tools on it with just two lines of code. It is designed to be highly flexible and extensible. With a single option you can change which tools should be enabled and which should be disabled. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications.

      Stanford CoreNLP integrates many of our NLP tools, including the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system, the sentiment analysis, and the bootstrapped pattern learning tools. The basic distribution provides model files for the analysis of English, but the engine is compatible with models for other languages. Below you can find packaged models for Chinese and Spanish, and Stanford NLP models for German and Arabic are usable inside CoreNLP.

      Stanford CoreNLP is written in Java and licensed under the GNU General Public License (v3 or later; in general Stanford NLP code is GPL v2+, but CoreNLP uses several Apache-licensed libraries, and so the composite is v3+). Source is included. Note that this is the full GPL, which allows many free uses, but not its use in proprietary software which is distributed to others. The download is 260 MB and requires Java 1.8+.