NodeJS CoreNLP Library

This project is under active development, please stay tuned for updates. More documentation and examples are comming.

This library connects to Stanford CoreNLP either via HTTP or by spawning processes. The first (HTTP) is the preferred method since it requires CoreNLP to initialize just once to serve many requests, it also avoids extra I/O given that the CLI method need to write temporary files to run.

Setup

1. Install the package:

npm i --save corenlp

2. Download Stanford CoreNLP

Via npm, run this command from your own project after having installed this library:

npm explore corenlp -- npm run corenlp:download

Once downloaded you can easily start the server by running

npm explore corenlp -- npm run corenlp:server

Or you can manually download the project from the Stanford's CoreNLP download section at: https://stanfordnlp.github.io/CoreNLP/download.html You may want to download, apart of the full package, other language models (see more on that page).

3. Configure Stanford CoreNLP

3.1. Using StanfordCoreNLPServer

# Run the server using all jars in the current directory (e.g., the CoreNLP home directory)
java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000

CoreNLP connects by default via StanfordCoreNLPServer, using port 9000. You can also opt to setup the connection differently:

import CoreNLP, { Properties, Pipeline, ConnectorServer } from 'corenlp';

const connector = new ConnectorServer({ dsn: 'http://localhost:9000' });
const props = new Properties({
  annotators: 'tokenize,ssplit,pos,lemma,ner,parse',
});
const pipeline = new Pipeline(props, 'English', connector);

3.2. Use CoreNLP via CLI

CoreNLP expects by default the StanfordCoreNLP package to be placed (unzipped) inside the path ${YOUR_NPM_PROJECT_ROOT}/corenlp/. You can also opt to setup the CLI interface differently:

import CoreNLP, { Properties, Pipeline, ConnectorCli } from 'corenlp';

const connector = new ConnectorCli({
  classPath: 'corenlp/stanford-corenlp-full-2017-06-09/*', // specify the paths relative to your npm project root
  mainClass: 'edu.stanford.nlp.pipeline.StanfordCoreNLP', // optional
  props: 'StanfordCoreNLP-spanish.properties', // optional
});
const props = new Properties({
  annotators: 'tokenize,ssplit,pos,lemma,ner,parse',
});
const pipeline = new Pipeline(props, 'English', connector);

4. Use it

// ... initialize pipeline first (see above)

const sent = new CoreNLP.simple.Sentence('Hello world');
pipeline.annotate(sent)
  .then(sent => {
    console.log(sent.words());
  })
  .catch(err => {
    console.log('err', err);
  });

Examples

NOTE1: The examples below assumes that StanfordCoreNLP is running on port 9000. NOTE2: The examples below assumes es6 syntax, if you use require, use as follows: var CoreNLP = require('corenlp').default;

English

import CoreNLP, { Properties, Pipeline } from 'corenlp';

const props = new Properties({
  annotators: 'tokenize,ssplit,pos,lemma,ner,parse',
});
const pipeline = new Pipeline(props, 'English'); // uses ConnectorServer by default

const sent = new CoreNLP.simple.Sentence('The little dog runs so fast.');
pipeline.annotate(sent)
  .then(sent => {
    console.log('parse', sent.parse());
    console.log(CoreNLP.util.Tree.fromSentence(sent).dump());
  })
  .catch(err => {
    console.log('err', err);
  });

Documentation

We will update this section soon. In the meantime, you can browse the project codebase and read the @jsdoc referenecs. In summary, this NodeJS library aims to replicate the CoreNLP Simple Java interface but in Javascript. There are some minor differences however, for example the need to call applyAnnotator asynchronously.

Properties
Pipeline
Service
ConnectorServer                   # https://stanfordnlp.github.io/CoreNLP/corenlp-server.html
ConnectorCli                      # https://stanfordnlp.github.io/CoreNLP/cmdline.html
CoreNLP
  simple                          # https://stanfordnlp.github.io/CoreNLP/simple.html
    Annotable
    Annotator
    Document
    Sentence
    Token
    annotator                     # https://stanfordnlp.github.io/CoreNLP/annotators.html
      TokenizerAnnotator          # https://stanfordnlp.github.io/CoreNLP/tokenize.html
      WordsToSentenceAnnotator    # https://stanfordnlp.github.io/CoreNLP/ssplit.html
      POSTaggerAnnotator          # https://stanfordnlp.github.io/CoreNLP/pos.html
      MorphaAnnotator             # https://stanfordnlp.github.io/CoreNLP/lemma.html
      NERClassifierCombiner       # https://stanfordnlp.github.io/CoreNLP/ner.html
      ParserAnnotator             # https://stanfordnlp.github.io/CoreNLP/parse.html
      DependencyParseAnnotator    # https://stanfordnlp.github.io/CoreNLP/depparse.html
      RelationExtractorAnnotator  # https://stanfordnlp.github.io/CoreNLP/relation.html
      DeterministicCorefAnnotator # https://stanfordnlp.github.io/CoreNLP/coref.html
  util
    Tree                          # http://www.cs.cornell.edu/courses/cs474/2004fa/lec1.pdf

Stanford CoreNLP Reference

https://github.com/stanfordnlp/CoreNLP

Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60.