compromise

natural language processing, actually in the browser

_{by Spencer Kelly and contributors}

var nlp = require('compromise')

nlp('Wee-ooh, I look just like buddy holly.').sentences().toPastTense()
// 'Wee-ooh, I looked just like buddy holly.'

nlp('..then consider me Miles Davis!').people().out('freq')
// [{ text:'Miles Davis', count:1 }]

210k     one javascript file

86%     on the Penn treebank

🙏   npm install compromise

IE9+     caniuse, youbetcha

_{with deliberate, rule-based nlp,}
compromise makes working with text easy

no jargon,   |   no config,   |   no training

Part-of-Speech tags	nouns, verbs, adjectives..	Verb conjugation	change tense of a verb or sentence
Number parsing	seven hundred and fifty -> 750	Named-entities	all the people, places, orgs..
Template-matches	match natural-language forms	Text cleanup	contractions, hyphenation, punctuation

_{🙌 you can do it!}

API docs   |   Demos list

Client-side!

<script src="https://unpkg.com/compromise@latest/builds/compromise.min.js"></script>
<script>
  var doc = nlp('dinosaur')

  doc.nouns().toPlural()
  console.log(doc.out('text'))
  // 'dinosaurs'
</script>

🌋 Server-side!

var nlp = require('compromise')

var doc = nlp('London is calling')
doc.sentences().toNegative()
// 'London is not calling'

Grab some words,

the match() syntax lets you grab non-specific words or patterns:

doc = nlp('Ludwig van Beethoven wrote to Josephine Brunsvik')

doc.match('#TitleCase van #LastName').out()
// 'Ludwig van Beethoven'

doc.match('#PastTense to').hyphenate().out()
// 'wrote-to'

common match-patterns have handy methods like .nouns(), or .people(),

doc.people().out('list')
// ['ludwig van beethoven', 'josephine brunsvik']

Plural/singular:

grab your noun-phrases, make em plural:

doc = nlp('a bottle of beer on the wall.')
doc.nouns().first().toPlural()
doc.out('text')
//'The bottles of beer on the wall.'

Number parsing:

parse written numbers, and change their form:

doc = nlp('ninety five thousand and fifty two')
doc.values().toNumber().out('text')
// '95052'

doc = nlp('the 23rd of December')
doc.values().toText()
doc.out('text')
// 'the twenty third of December'

Normalization:

some wrappers for common changes:

doc = nlp("the guest-singer's björk at seven thirty.").normalize().out('text')
// 'The guest singer is Bjork at 7:30.'

Tense:

all your base are belong:

let doc = nlp('she sells seashells by the seashore.')
doc.sentences().toFutureTense().out('text')
//'she will sell seashells...'

doc.verbs().conjugate()
// [{ PastTense: 'sold',
//    Infinitive: 'sell',
//    Gerund: 'selling', ...
// }]

Named-entity spotting:

find the people, places, organizations:

doc = nlp('that opera about richard nixon visiting china')
doc.topics().data()
// [
//   { text: 'richard nixon' },
//   { text: 'china' }
// ]

Error correction:

make it say what you'd like:

var lexicon={
  'boston': 'MusicalGroup'
}
doc = nlp('i heard Boston\'s set in Chicago', lexicon)
doc.match('#MusicalGroup').length
// 1

//alternatively, fix it all 'in-post':
doc.match('heard #Possessive set').terms(1).tag('MusicalGroup')
doc.match('#MusicalGroup').length
// 1

Handy outputs:

get some data:

doc = nlp('We like Roy! We like Roy!').sentences().out('array')
// ['We like Roy!', 'We like Roy!']

doc = nlp('Tony Hawk').out('html')
/*
<span>
  <span class="nl-Person nl-FirstName">Tony</span>
  <span>&nbsp;</span>
  <span class="nl-Person nl-LastName">Hawk</span>
</span>
*/

and yes, ofcourse, there's a lot more stuff.

Join in - we're fun, we're using semver, and moving fast. get involved

Twitter

Slack group

Mailing-list

Applications

Pull-requests

Don't forget about:

• ✨   naturalNode - decidedly fancier, statistical nlp in javascript
• 🍭   SuperScript - clever conversation engine in js
• 💗   NodeBox Linguistics - conjugation, inflection in javascript
• 🎀   reText - very impressive text utilities in javascript
• 💎   jsPos - javascript build of the time-tested Brill-tagger
• 🚗   spaCy - speedy, multilingual tagger in C/python

For the former promise-library, see jnewman/compromise (Thanks Joshua!)

(also don't forget 🙇 NLTK, GATE, Stanford, and Illinois toolkit ) ❤️️