wink-pos-tagger

English Part-of-speech (POS) tagger

Perform part-of-speech tagging of english sentences using wink-pos-tagger. It is based on transformation based learning (TBL) approach pioneered by Eric Brill.

Optimized for performance, it pos-tags and lemmatizes over 525,000 tokens per second with an accuracy of 93.2% on the standard WSJ22-24 test set. This was benchmarked on 2.2 GHz Intel Core i7 machine with 16GB RAM using its tagRawTokens() API.

Installation

Use npm to install:

npm install wink-pos-tagger --save

Getting Started

The code below illustrates the steps required to pos tag a sentence:

// Load wink-pos-tagger.
var posTagger = require( 'wink-pos-tagger' );

// Create an instance of the pos tagger.
var tagger = posTagger();

// Tag the sentence using the tag sentence api.
tagger.tagSentence( 'He is trying to fish for fish in the lake.' );
// -> [ { value: 'He', tag: 'word', normal: 'he', pos: 'PRP' },
//      { value: 'is', tag: 'word', normal: 'is', pos: 'VBZ', lemma: 'be' },
//      { value: 'trying', tag: 'word', normal: 'trying', pos: 'VBG', lemma: 'try' },
//      { value: 'to', tag: 'word', normal: 'to', pos: 'TO' },
//      { value: 'fish', tag: 'word', normal: 'fish', pos: 'VB', lemma: 'fish' },
//      { value: 'for', tag: 'word', normal: 'for', pos: 'IN' },
//      { value: 'fish', tag: 'word', normal: 'fish', pos: 'NN', lemma: 'fish' },
//      { value: 'in', tag: 'word', normal: 'in', pos: 'IN' },
//      { value: 'the', tag: 'word', normal: 'the', pos: 'DT' },
//      { value: 'lake', tag: 'word', normal: 'lake', pos: 'NN', lemma: 'lake' },
//      { value: '.', tag: 'punctuation', normal: '.', pos: '.' } ]

Notice the way instances of the word "fish" have been tagged as verb and noun.

Documentation

Check out the pos tagger API documentation to learn more.

Need Help?

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

About wink

Wink is a family of open source packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.

Copyright & License

It is licensed under the terms of the MIT License.