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spencermountain
9.8K Stars 625 Forks MIT License 4.4K Commits 76 Opened issues

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modest natural-language processing

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compromise
modest natural language processing
npm install compromise

isn't it weird how we can write text, but not parse it?
    ᔐᖜ↬- and how we can't get the information back out?⇬

it's like we've agreed that
text is a dead-end.

and the knowledge in it
should not really be used.

compromise tries its best to parse text.
it is small, quick, and often good-enough.
it is not as smart as you'd think.

.match():

interpret and match text:

let doc = nlp(entireNovel)
doc.match('the #Adjective of times').text()
// "the blurst of times?"
if (doc.has('simon says #Verb') === false) {
  return null
}

.verbs():

conjugate and negate verbs in any tense:

let doc = nlp('she sells seashells by the seashore.')
doc.verbs().toPastTense()
doc.text()
// 'she sold seashells by the seashore.'

.nouns():

play between plural, singular and possessive forms:

let doc = nlp('the purple dinosaur')
doc.nouns().toPlural()
doc.text()
// 'the purple dinosaurs'

.numbers():

interpret plain-text numbers

nlp.extend(require('compromise-numbers'))

let doc = nlp('ninety five thousand and fifty two') doc.numbers().add(2) doc.text() // 'ninety five thousand and fifty four'

.topics():

names/places/orgs, tldr:

let doc = nlp(buddyHolly)
doc.people().if('mary').json()
// [{text:'Mary Tyler Moore'}]

let doc = nlp(freshPrince) doc.places().first().text() // 'West Phillidelphia'

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

.contractions():

handle implicit terms:

let doc = nlp("we're not gonna take it, no we ain't gonna take it.")

// match an implicit term doc.has('going') // true

// transform doc.contractions().expand() dox.text() // 'we are not going to take it, no we are not going to take it.'

Use it on the client-side:

as an es-module:

import nlp from 'compromise'

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

compromise is 180kb (minified):

it's pretty fast. It can run on keypress:

it works mainly by conjugating all forms of a basic word list.

The final lexicon is ~14,000 words:

you can read more about how it works, here. it's weird.

<!-- -->

.extend():

decide how words get interpreted:

let myWords = {
  kermit: 'FirstName',
  fozzie: 'FirstName',
}
let doc = nlp(muppetText, myWords)

or make heavier changes with a compromise-plugin.

const nlp = require('compromise')

nlp.extend((Doc, world) => { // add new tags world.addTags({ Character: { isA: 'Person', notA: 'Adjective', }, })

// add or change words in the lexicon world.addWords({ kermit: 'Character', gonzo: 'Character', })

// add methods to run after the tagger world.postProcess(doc => { doc.match('light the lights').tag('#Verb . #Plural') })

// add a whole new method Doc.prototype.kermitVoice = function () { this.sentences().prepend('well,') this.match('i [(am|was)]').prepend('um,') return this } })

Docs:

gentle introduction:
Documentation:

| Concepts | API | Plugins | | ------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------: | | Accuracy | Accessors | Adjectives | | Caching | Constructor-methods | Dates | | Case | Contractions | Export | | Filesize | Insert | Hash | | Internals | Json | Html | | Justification | Lists | Keypress | | Lexicon | Loops | Ngrams | | Match-syntax | Match | Numbers | | Performance | Nouns | Paragraphs | | Plugins | Output | Scan | | Projects | Selections | Sentences | | Tagger | Sorting | Syllables | | Tags | Split | Pronounce | | | Tokenization | Text | Strict | | Named-Entities | Utils | Penn-tags | | Whitespace | Verbs | Typeahead | | World data | Normalization | | | Fuzzy-matching | Typescript | |

Talks:
Articles:
Some fun Applications:

API:

Constructor

(these methods are on the

nlp
object)

  • .tokenize() - parse text without running POS-tagging
  • .extend() - mix in a compromise-plugin
  • .fromJSON() - load a compromise object from
    .json()
    result
  • .verbose() - log our decision-making for debugging
  • .version() - current semver version of the library
  • .world() - grab all current linguistic data
  • .parseMatch() - pre-parse any match statements for faster lookups
Utils
  • .all() - return the whole original document ('zoom out')
  • .found [getter] - is this document empty?
  • .parent() - return the previous result
  • .parents() - return all of the previous results
  • .tagger() - (re-)run the part-of-speech tagger on this document
  • .wordCount() - count the # of terms in the document
  • .length [getter] - count the # of characters in the document (string length)
  • .clone() - deep-copy the document, so that no references remain
  • .cache({}) - freeze the current state of the document, for speed-purposes
  • .uncache() - un-freezes the current state of the document, so it may be transformed
Accessors
Match

(all match methods use the match-syntax.)

  • .match('') - return a new Doc, with this one as a parent
  • .not('') - return all results except for this
  • .matchOne('') - return only the first match
  • .if('') - return each current phrase, only if it contains this match ('only')
  • .ifNo('') - Filter-out any current phrases that have this match ('notIf')
  • .has('') - Return a boolean if this match exists
  • .lookBehind('') - search through earlier terms, in the sentence
  • .lookAhead('') - search through following terms, in the sentence
  • .before('') - return all terms before a match, in each phrase
  • .after('') - return all terms after a match, in each phrase
  • .lookup([]) - quick find for an array of string matches
Case
Whitespace
  • .pre('') - add this punctuation or whitespace before each match
  • .post('') - add this punctuation or whitespace after each match
  • .trim() - remove start and end whitespace
  • .hyphenate() - connect words with hyphen, and remove whitespace
  • .dehyphenate() - remove hyphens between words, and set whitespace
  • .toQuotations() - add quotation marks around these matches
  • .toParentheses() - add brackets around these matches
Tag
  • .tag('') - Give all terms the given tag
  • .tagSafe('') - Only apply tag to terms if it is consistent with current tags
  • .unTag('') - Remove this term from the given terms
  • .canBe('') - return only the terms that can be this tag
Loops
  • .map(fn) - run each phrase through a function, and create a new document
  • .forEach(fn) - run a function on each phrase, as an individual document
  • .filter(fn) - return only the phrases that return true
  • .find(fn) - return a document with only the first phrase that matches
  • .some(fn) - return true or false if there is one matching phrase
  • .random(fn) - sample a subset of the results
Insert
Transform
Output
Selections
Subsets

Plugins:

These are some helpful extensions:

Adjectives

npm install compromise-adjectives
Dates

npm install compromise-dates
Numbers

npm install compromise-numbers
Export

npm install compromise-export
  • .export() - store a parsed document for later use
  • nlp.load() - re-generate a Doc object from .export() results
Html

npm install compromise-html
  • .html({}) - generate sanitized html from the document
Hash

npm install compromise-hash
  • .hash() - generate an md5 hash from the document+tags
  • .isEqual(doc) - compare the hash of two documents for semantic-equality
Keypress

npm install compromise-keypress
Ngrams

npm install compromise-ngrams
Paragraphs

npm install compromise-paragraphs
this plugin creates a wrapper around the default sentence objects.
Sentences

npm install compromise-sentences
Strict-match

npm install compromise-strict
Syllables

npm install compromise-syllables
  • .syllables() - split each term by its typical pronounciation
Penn-tags

npm install compromise-penn-tags

Typescript

we're committed to typescript/deno support, both in main and in the official-plugins:

import nlp from 'compromise'
import ngrams from 'compromise-ngrams'
import numbers from 'compromise-numbers'

const nlpEx = nlp.extend(ngrams).extend(numbers)

nlpEx('This is type safe!').ngrams({ min: 1 }) nlpEx('This is type safe!').numbers()

Partial-builds

or if you don't care about POS-tagging, you can use the tokenize-only build: (90kb!)

Limitations:

  • slash-support: We currently split slashes up as different words, like we do for hyphens. so things like this don't work: nlp('the koala eats/shoots/leaves').has('koala leaves') //false

  • inter-sentence match: By default, sentences are the top-level abstraction. Inter-sentence, or multi-sentence matches aren't supported without a plugin: nlp("that's it. Back to Winnipeg!").has('it back')//false

  • nested match syntax: the danger beauty of regex is that you can recurse indefinitely. Our match syntax is much weaker. Things like this are not (yet) possible: doc.match('(modern (major|minor))? general') complex matches must be achieved with successive .match() statements.

  • dependency parsing: Proper sentence transformation requires understanding the syntax tree of a sentence, which we don't currently do. We should! Help wanted with this.

FAQ

    ☂️ Isn't javascript too...

      yeah it is!
      it wasn't built to compete with NLTK, and may not fit every project.
      string processing is synchronous too, and parallelizing node processes is weird.
      See here for information about speed & performance, and here for project motivations

    💃 Can it run on my arduino-watch?

      Only if it's water-proof!
      Read quick start for running compromise in workers, mobile apps, and all sorts of funny environments.

    🌎 Compromise in other Languages?

      we've got work-in-progress forks for German and French, in the same philosophy.
      and need some help.

    ✨ Partial builds?

      we do offer a compromise-tokenize build, which has the POS-tagger pulled-out.
      but otherwise, compromise isn't easily tree-shaken.
      the tagging methods are competitive, and greedy, so it's not recommended to pull things out.
      Note that without a full POS-tagging, the contraction-parser won't work perfectly. ((spencer's cool) vs. (spencer's house))
      It's recommended to run the library fully.

See Also:

MIT

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