Bayes Sandbox (Updated)

 wink-expression-extractor.js

Takes "input.txt" produces "output.json"

const winkNLP = require('wink-nlp');

const model = require('wink-eng-lite-web-model');

const fs = require('fs');

const nlp = winkNLP(model);

const text = fs.readFileSync('input.txt', 'utf-8');

// Obtain "its" helper to extract item properties.

const its = nlp.its;

// Obtain "as" reducer helper to reduce a collection.

const as = nlp.as;

const patterns = [

  {

    name: 'nounPhrase',

    label: 'nounPhrase',

    patterns: [ '[|DET] [|ADJ] [NOUN|PROPN]' ]

  },

  {

    name: 'verbPhrase',

    label: 'verbPhrase',

    patterns: [ '[|ADV] [|PARTICLE] [|ADJ] [|NOUN] [VERB]' ]

  }

];

nlp.learnCustomEntities(patterns);

const doc = nlp.readDoc(text);

const entities = doc.customEntities().out(its.detail);

fs.writeFile('output.json', JSON.stringify(entities), (err) => {

  if (err) throw err;

  console.log('The output has been written to output.json');

});

Input.txt can be any textfile. This will extract some noun and verb phrases that match specific patterns and then output them to a json file. 

Bayes_sandbox.js

Takes "output.json"

Expected output: "verbPhrase"

const fs = require('fs');

// Read the contents of the file into a string

const jsonString = fs.readFileSync('output.json', 'utf-8');

// Parse the JSON string into a JavaScript object

const jsonObj = JSON.parse(jsonString);

// Load Naive Bayes Text Classifier

var Classifier = require( 'wink-naive-bayes-text-classifier' );

// Instantiate

var nbc = Classifier();

// Load wink nlp and its model

const winkNLP = require( 'wink-nlp' );

// Load language model

const model = require( 'wink-eng-lite-web-model' );

const nlp = winkNLP( model );

const its = nlp.its;

const prepTask = function ( text ) {

  const tokens = [];

  nlp.readDoc(text)

      .tokens()

      // Use only words ignoring punctuations etc and from them remove stop words

      .filter( (t) => ( t.out(its.type) === 'word' && !t.out(its.stopWordFlag) ) )

      // Handle negation and extract stem of the word

      .each( (t) => tokens.push( (t.out(its.negationFlag)) ? '!' + t.out(its.stem) : t.out(its.stem) ) );

  return tokens;

};

nbc.definePrepTasks( [ prepTask ] );

// Configure behavior

nbc.defineConfig( { considerOnlyPresence: true, smoothingFactor: 0.5 } );

// Train!

jsonObj.forEach(obj =>

    nbc.learn(obj.value, obj.type));

nbc.consolidate();

console.log( nbc.predict( 'failing stars' ) );

This file uses the data extracted from the first file to train a Bayes classifier. It then should correctly predict that "failing stars" is a verbal phrase.