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Natural Language Processing
Stanford CoreNLP provides a set of human language technology tools. It can 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 syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc.
Choose Stanford CoreNLP if you need:
An integrated NLP toolkit with a broad range of grammatical analysis tools A fast, robust annotator for arbitrary texts, widely used in production A modern, regularly updated package, with the overall highest quality text analytics Support for a number of major (human) languages Available APIs for most major modern programming languages Ability to run as a simple web service
Stanford CoreNLP’s goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. A tool pipeline can be run on a piece of plain text with just two lines of code. CoreNLP is designed to be highly flexible and extensible. With a single option you can change which tools should be enabled and disabled. Stanford CoreNLP integrates many of Stanford’s NLP tools, including
- the part-of-speech (POS) tagger,
- the named entity recognizer (NER),
- the parser,
- the coreference resolution system,
- sentiment analysis,
- bootstrapped pattern learning and
- the open information extraction tools.
Stanford CoreNLP can be downloaded via the link below. This will download a large (~500 MB) zip file containing (1) the CoreNLP code jar, (2) the CoreNLP models jar (required in your classpath for most tasks), (3) the libraries required to run CoreNLP, and (4) documentation / source code for the project. Unzip this file, open the folder that results and you’re ready to use it. https://stanfordnlp.github.io/CoreNLP/download.html
Alternatively, Stanford CoreNLP is available on Maven Central.
The basic distribution provides model files for the analysis of well-edited English, but the engine is compatible with models for other languages. In the table above, we provide packaged models for Arabic, Chinese, French, German, and Spanish. We also provide two jars that contain all of our English models, which include various variant models, and in particular models optimized for working with uncased English (e.g., mostly or all either uppercase or lowercase).
Stanford CoreNLP is written in Java; recent releases require Java 1.8+.You need to have Java installed to run CoreNLP. However, you can interact with CoreNLP via the command-line or its web service; many people use CoreNLP while writing their own code in Javascript, Python, or some other language. You can use Stanford CoreNLP from the command-line, via its original Java programmatic API, via the object-oriented simple API, via third party APIs for most major modern programming languages, or via a web service. It works on Linux, macOS, and Windows.
The full Stanford CoreNLP is licensed under the GNU General Public License v3 or later. More precisely, all the Stanford NLP code is GPL v2+, but CoreNLP uses some Apache-licensed libraries, and so our understanding is that the the composite is correctly licensed as v3+. You can run almost all of CoreNLP under GPL v2; you simply need to omit the time-related libraries, and then you lose the functionality of SUTime. Note that the license is the full GPL, which allows many free uses, but not its use in proprietary software which is distributed to others. For distributors of proprietary software, CoreNLP is also available from Stanford under a commercial licensing You can contact at java-nlp-support@lists.stanford.edu.