retext-keywords

retext-keywords

Keyword extraction with retext.

Installation

npm:

$ npm install retext-keywords

[Duo](http://duojs.org/#getting-started):

```javascript
var keywords = require('wooorm/retext-keywords');

Usage

var Retext = require('retext');
var keywords = require('retext-keywords');

var retext = new Retext().use(keywords);

retext.parse(
    /* First three paragraphs on Term Extraction from Wikipedia:
     * http://en.wikipedia.org/wiki/Terminology_extraction */
    'Terminology mining, term extraction, term recognition, or ' +
    'glossary extraction, is a subtask of information extraction. ' +
    'The goal of terminology extraction is to automatically extract ' +
    'relevant terms from a given corpus.' +
    '\n\n' +
    'In the semantic web era, a growing number of communities and ' +
    'networked enterprises started to access and interoperate through ' +
    'the internet. Modeling these communities and their information ' +
    'needs is important for several web applications, like ' +
    'topic-driven web crawlers, web services, recommender systems, ' +
    'etc. The development of terminology extraction is essential to ' +
    'the language industry.' +
    '\n\n' +
    'One of the first steps to model the knowledge domain of a ' +
    'virtual community is to collect a vocabulary of domain-relevant ' +
    'terms, constituting the linguistic surface manifestation of ' +
    'domain concepts. Several methods to automatically extract ' +
    'technical terms from domain-specific document warehouses have ' +
    'been described in the literature.' +
    '\n\n' +
    'Typically, approaches to automatic term extraction make use of ' +
    'linguistic processors (part of speech tagging, phrase chunking) ' +
    'to extract terminological candidates, i.e. syntactically ' +
    'plausible terminological noun phrases, NPs (e.g. compounds ' +
    '"credit card", adjective-NPs "local tourist information office", ' +
    'and prepositional-NPs "board of directors" - in English, the ' +
    'first two constructs are the most frequent). Terminological ' +
    'entries are then filtered from the candidate list using ' +
    'statistical and machine learning methods. Once filtered, ' +
    'because of their low ambiguity and high specificity, these terms ' +
    'are particularly useful for conceptualizing a knowledge domain ' +
    'or for supporting the creation of a domain ontology. Furthermore, ' +
    'terminology extraction is a very useful starting point for ' +
    'semantic similarity, knowledge management, human translation ' +
    'and machine translation, etc.',
    function (err, tree) {
        tree.keywords();
        /*
         * Array[5]
         * ├─ 0: Object
         * |     ├─ stem: "terminolog"
         * |     ├─ score: 1
         * |     └─ nodes: Array[7]
         * ├─ 1: Object
         * |     ├─ stem: "term"
         * |     ├─ score: 1
         * |     └─ nodes: Array[7]
         * ├─ 2: Object
         * |     ├─ stem: "extract"
         * |     ├─ score: 1
         * |     └─ nodes: Array[7]
         * ├─ 3: Object
         * |     ├─ stem: "web"
         * |     ├─ score: 0.5714285714285714
         * |     └─ nodes: Array[4]
         * └─ 4: Object
         *       ├─ stem: "domain"
         *       ├─ score: 0.5714285714285714
         *       └─ nodes: Array[4]
         */
    }
);

API

TextOM.Parent#keywords(options?)

Extract keywords, based on the number of times they (nouns) occur in text.

/* See above for an example, and output. */

/* To *not* limit keyword-count: */
tree.keywords({'minimum' : Infinity});

Options:

• minimum (non-negative integer number) — Return at least (when possible) minimum keywords.

Results: An array, containing match-objects:

• stem: The stem of the word (see retext-porter-stemmer);
• score: A value between 0 and (including) 1. The first match has a score of 1;
• nodes: An array containing all matched WordNodes.

TextOM.Parent#keyphrases(options?)

Extract keyphrases, based on the number of times they (one or more nouns) occur in text.

tree.keyphrases();
/*
 * Array[6]
 * ├─ 0: Object
 * |     ├─ stems: Array[2]
 * |     |         ├─ 0: "terminolog"
 * |     |         └─ 1: "extract"
 * |     ├─ score: 1
 * |     └─ nodes: Array[3]
 * ├─ 1: Object
 * |     ├─ stems: Array[1]
 * |     |         └─ 0: "term"
 * |     ├─ score: 0.46153846153846156
 * |     └─ nodes: Array[3]
 * ├─ 2: Object
 * |     ├─ stems: Array[2]
 * |     |         ├─ 0: "term"
 * |     |         └─ 1: "extract"
 * |     ├─ score: 0.4444444444444444
 * |     └─ nodes: Array[2]
 * ├─ 3: Object
 * |     ├─ stems: Array[2]
 * |     |         ├─ 0: "knowledg"
 * |     |         └─ 1: "domain"
 * |     ├─ score: 0.20512820512820512
 * |     └─ nodes: Array[2]
 * └─ 5: Object
 *       ├─ stems: Array[1]
 *       |         └─ 0: "commun"
 *       ├─ score: 0.15384615384615385
 *       └─ nodes: Array[3]
*/

/* To *not* limit phrase-count: */
tree.keyphrases({'minimum' : Infinity});

Options:

• minimum (non-negative integer number) — Return at least (when possible) minimum phrases.

Results: An array, containing match-objects:

• stems: An array containing the stems of all matched word nodes inside the phrase(s);
• score: A value between 0 and (including) 1. The first match has a score of 1;
• nodes: An array containing arrays of WordNodes.

Benchmark

On a MacBook Air, keywords() runs about 3,784 op/s on a big section / small article.

             A big section (10 paragraphs)
  4,026 op/s » Finding keywords
    625 op/s » Finding keyphrases

             A big article (100 paragraphs)
    438 op/s » Finding keywords
     59 op/s » Finding keyphrases

License

MIT © Titus Wormer