POS Tagger

This is a part-of-speech tagger based on Eric Brill’s transformational algorithm. It needs a lexicon and a set of transformation rules.


First a lexicon is created. First parameter is language (EN for English and DU for Dutch), second is default category. Optionally, a third parameter can be supplied that is the default category for capitalised words.

var natural = require("natural");
const language = "EN"
const defaultCategory = 'N';
const defaultCategoryCapitalized = 'NNP';

var lexicon = new natural.Lexicon(language, defaultCategory, defaultCategoryCapitalized);
var ruleSet = new natural.RuleSet('EN');
var tagger = new natural.BrillPOSTagger(lexicon, ruleSet);

Then a ruleset is created, as follows. Parameter is the language.

var ruleSet = new natural.RuleSet('EN');

Now a tagger can be created by passing lexicon and ruleset:

var tagger = new natural.BrillPOSTagger(lexicon, ruleSet);
var sentence = ["I", "see", "the", "man", "with", "the", "telescope"];

This outputs the following:

Sentence {
   [ { token: 'I', tag: 'NN' },
     { token: 'see', tag: 'VB' },
     { token: 'the', tag: 'DT' },
     { token: 'man', tag: 'NN' },
     { token: 'with', tag: 'IN' },
     { token: 'the', tag: 'DT' },
     { token: 'telescope', tag: 'NN' } ] }


The lexicon is a JSON file that has the following structure:

  "word1": ["cat1"],
  "word2": ["cat2", "cat3"],

Words may have multiple categories in the lexicon file. The tagger uses only the first category specified.

Specifying transformation rules

Transformation rules are specified a JSON file as follows:

  "rules": [

This particular means that if the category of the current position is OLD_CAT and the predicate is true, the category is replaced by NEW_CAT. The predicate may use the parameter in different ways: sometimes the parameter is used for specifying the outcome of the predicate:


This means that if the outcome of predicate CURRENT-WORD-IS-NUMBER is YES, the category is replaced by CD. The parameter can also be used to check the category of a word in the sentence:


Here the category of the previous word must be DT for the rule to be applied.


The tagger applies transformation rules that may change the category of words. The input sentence is a Sentence object with tagged words. The tagged sentence is processed from left to right. At each step all rules are applied once; rules are applied in the order in which they are specified. Algorithm:

Brill_POS_Tagger.prototype.applyRules = function(sentence) {
  for (var i = 0, size = sentence.taggedWords.length; i < size; i++) {
    this.ruleSet.getRules().forEach(function(rule) {
      rule.apply(sentence, i);
  return sentence;

The output is a Sentence object just like the input sentence.

Adding a predicate

Predicates are defined in module lib/RuleTemplates.js. In that file predicate names are mapped to metadata for generaring transformation rules. The following properties must be supplied:

  • Name of the predicate
  • A function that evaluates the predicate (should return a boolean)
  • A window [i, j] that defines the span of the predicate in the sentence relative to the current position
  • The number of parameter the predicate needs: 0, 1 or 2
  • If relevant, a function for parameter 1 that returns its possible values at the current position in the sentence (for generating rules in training)
  • If relevant, a function for parameter 2 that returns its possible values at the current position in the sentence (for training)

A typical entry for a rule templates looks like this:

    // maps to the predicate function
    "function": next_tag_is,
    // Minimum required window before or after current position to be a relevant predicate
    "window": [0, 1],
    // The number of parameters the predicate takes
    "nrParameters": 1,
    // Function that returns relevant values for parameter 1
    "parameter1Values": nextTagParameterValues

A predicate function accepts a Sentence object, the current position in the sentence that should be tagged, and the outcome(s) of the predicate. An example of a predicate that checks the category of the current word:

function next_tag_is(sentence, i, parameter) {
  if (i < sentence.taggedWords.length - 1) {
    return(sentence.taggedWords[i + 1][1] === parameter);
  else {

A values function for a parameter returns an array all possible parameter values given a location in a tagged sentence.

function nextTagParameterValues(sentence, i) {
  if (i < sentence.length - 1) {
    return [sentence[i + 1].tag];
  else {
    return [];


The trainer allows to learn a new set of transformation rules from a corpus. It takes as input a tagged corpus and a set of rule templates. The algorithm generates positive rules (rules that apply at some location in the corpus) from the templates and iteratively extends and optimises the rule set.

First, a corpus should be loaded. Currently, the format of Brown corpus is supported. Then a lexicon can be created from the corpus. The lexicon is needed for tagging the sentences before the learning algorithm is applied.

var natural = require(natural);
const JSON_FLAG = 2;

var brownCorpus = require('../lib/natural/brill_pos_tagger/lib/Corpus');
var corpus = new Corpus(brownCorpus, JSON_FLAG, natural.Sentence);
var lexicon = corpus.buildLexicon();

The next step is to create a set of rule templates from which the learning algorithm can generate transformation rules. Rule templates are defined in PredicateMapping.js.

var templateNames = [
var templates = templateNames.map(function(name) {
  return new natural.RuleTemplate(name);

Using lexicon and rule templates we can now start the trainer as follows.

var trainer = new natural.BrillPOSTrainer(/* optional threshold */);
var ruleSet = trainer.train(corpus, templates, lexicon);

A threshold value can be passed to constructor. Transformation rules with a score below the threshold are removed after training. The train method returns a set of transformation rules that can be used to create a POS tagger as usual. Also you can output the rule set in the right format for later usage.



Now we can apply the lexicon and rule set to a test set.

var tester = new natural.BrillPOSTester();
var tagger = new natural.BrillPOSTagger(lexicon, ruleSet);
var scores = tester.test(corpora[1], tagger);

The test method returns an array of two percentages: first percentage is the ratio of right tags after tagging with the lexicon; second percentage is the ratio of right tags after applying the transformation rules.

console.log("Test score lexicon " + scores[0] + "%");
console.log("Test score after applying rules " + scores[1] + "%");


  • Part of speech tagger by Percy Wegmann, https://code.google.com/p/jspos/
  • Node.js version of jspos: https://github.com/neopunisher/pos-js
  • A simple rule-based part of speech tagger, Eric Brill, Published in: Proceeding ANLC ‘92 Proceedings of the third conference on Applied natural language processing, Pages 152-155. http://dl.acm.org/citation.cfm?id=974526
  • Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging, Lance A. Ramshaw and Mitchell P. Marcus. http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d4/data/pdf/anthology-PDF/W/W94/W94-0111.pdf
  • Brown Corpus: https://en.wikipedia.org/wiki/Brown_Corpus