Name: compromise
Author: Spencer Kelly

<div align="center"> <img height="15px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <div><b>compromise</b></div> <img src="https://user-images.githubusercontent.com/399657/68222691-6597f180-ffb9-11e9-8a32-a7f38aa8bded.png"/> <div>modest natural language processing</div> <div><code>npm install compromise</code></div> <div align="center"> <sub> by <a href="https://spencermounta.in/">Spencer Kelly</a> and <a href="https://github.com/spencermountain/compromise/graphs/contributors"> many contributors </a> </sub> </div> <img height="22px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> </div> <div align="center"> <div> <a href="https://npmjs.org/package/compromise"> <img src="https://img.shields.io/npm/v/compromise.svg?style=flat-square" /> </a> <a href="https://codecov.io/gh/spencermountain/compromise"> <img src="https://codecov.io/gh/spencermountain/compromise/branch/master/graph/badge.svg" /> </a> <a href="https://bundlephobia.com/result?p=compromise"> <img src="https://img.shields.io/bundlephobia/min/compromise"/>  </a> </div> <div align="center"> <sub> <a href="https://github.com/nlp-compromise/fr-compromise">french</a> • <a href="https://github.com/nlp-compromise/de-compromise">german</a> • <a href="https://github.com/nlp-compromise/it-compromise">italian</a> • <a href="https://github.com/nlp-compromise/es-compromise">spanish</a> </sub> </div> </div>  <img height="25px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <div align="left"> don't you find it strange, <br/> <ul> <img height="2px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <sub>how easy <b>text</b> is to <b>make</b>,</sub> <br/> <img height="2px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

<i>↬<sub>ᔐᖜ</sub><b>↬</b></i> <sub></sub> and how hard it is to actually <b>parse</b> and <i>use</i>?

</ul> </div>  <img height="45px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <div align="left"> <img height="10px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>compromise <i><a href="https://observablehq.com/@spencermountain/compromise-justification">tries its best</a></i> to turn text into data. <br/> <img height="30px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>it makes limited and sensible decisions. <br/> <sub > <img height="15px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> it's not as smart as you'd think. </sub> <img height="45px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>  </div> <img height="30px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

import nlp from 'compromise'

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

<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <div align="left"> <i>don't be fancy, at all:</i> </div>

if (doc.has('simon says #Verb')) {
  return true
}

<img height="30px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <div align="center"> <img height="50px" src="https://user-images.githubusercontent.com/399657/68221814-05ed1680-ffb8-11e9-8b6b-c7528d163871.png"/> </div> <div align="left"> <i>grab parts of the text:</i> </div>

let doc = nlp(entireNovel)
doc.match('the #Adjective of times').text()
// "the blurst of times?"

<div align="right"> <a href="https://docs.compromise.cool/compromise-match">match docs</a> </div> <div align="center"> <img height="50px" src="https://user-images.githubusercontent.com/399657/68221837-0d142480-ffb8-11e9-9d30-90669f1b897c.png"/> </div>  <img height="30px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

import plg from 'compromise-speech'
nlp.extend(plg)

let doc = nlp('Milwaukee has certainly had its share of visitors..')
doc.compute('syllables')
doc.places().json()
/*
[{
  "text": "Milwaukee",
  "terms": [{
    "normal": "milwaukee",
    "syllables": ["mil", "wau", "kee"]
  }]
}]
*/

avoid the problems of brittle parsers:

let doc = nlp("we're not gonna take it..")

doc.has('gonna') // true
doc.has('going to') // true (implicit)

// transform
doc.contractions().expand()
doc.text()
// 'we are not going to take it..'

<div align="right"> <a href="https://docs.compromise.cool/compromise-contractions">contraction docs</a> </div> <div align="center"> <img height="50px" src="https://user-images.githubusercontent.com/399657/68221814-05ed1680-ffb8-11e9-8b6b-c7528d163871.png"/> </div>  <img height="30" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

and whip stuff around like it's data:

let doc = nlp('ninety five thousand and fifty two')
doc.numbers().add(20)
doc.text()
// 'ninety five thousand and seventy two'

<div align="right"> <a href="https://docs.compromise.cool/compromise-values">number docs</a> </div> <div align="center"> <img height="50px" src="https://user-images.githubusercontent.com/399657/68221837-0d142480-ffb8-11e9-9d30-90669f1b897c.png"/> </div>  <img height="30" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

<sub>-because it actually is-</sub>

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

Use it on the client-side:

<script src="https://unpkg.com/compromise"></script>
<script>
  var doc = nlp('two bottles of beer')
  doc.numbers().minus(1)
  document.body.innerHTML = doc.text()
  // 'one bottle of beer'
</script>

or likewise:

import nlp from 'compromise'

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

compromise is ~250kb (minified):

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

it works mainly by <a href="https://observablehq.com/@spencermountain/verbs">conjugating all forms</a> of a basic word list.

The final lexicon is <a href="https://observablehq.com/@spencermountain/compromise-lexicon">~14,000 words</a>:

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

<img height="75px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>  <p align="left"> <sub>okay -</sub> <h1> <code>compromise/one</code> </h1> <p align="center">A <code>tokenizer</code> of words, sentences, and punctuation.</p> <img height="15px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <p>

import nlp from 'compromise/one'

let doc = nlp("Wayne's World, party time")
let data = doc.json()
/* [{
  normal:"wayne's world party time",
    terms:[{ text: "Wayne's", normal: "wayne" },
      ...
      ]
  }]
*/

<div align="right"> <a href="https://docs.compromise.cool/compromise-tokenization">tokenizer docs</a> </div>

<b>compromise/one</b> splits your text up, wraps it in a handy API,

<ul> <sub>and does nothing else -</sub> </ul> <img height="25px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

<b>/one</b> is quick - most sentences take a 10th of a millisecond.

It can do <b>~1mb</b> of text a second - or 10 wikipedia pages.

<i>Infinite jest</i> takes 3s.

<div align="right"> You can also parallelize, or stream text to it with <a href="https://github.com/spencermountain/compromise/tree/master/plugins/speed">compromise-speed</a>. </div>  <img height="60px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>  <p align="center"> <h1 align="left"> <code>compromise/two</code> </h1> <p align="center">A <code>part-of-speech</code> tagger, and grammar-interpreter.</p> <img height="15px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <p>

import nlp from 'compromise/two'

let doc = nlp("Wayne's World, party time")
let str = doc.match('#Possessive #Noun').text()
// "Wayne's World"

<div align="right"> <a href="https://docs.compromise.cool/compromise-tagger">tagger docs</a> </div> <p> <img height="25px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> </p> <b>compromise/two</b> automatically calculates the very basic grammar of each word.

<sub>this is more useful than people sometimes realize.</sub>

Light grammar helps you write cleaner templates, and get closer to the information.

<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

compromise has <b>83 tags</b>, arranged in <a href="https://observablehq.com/@spencermountain/compromise-tags">a handsome graph</a>.

<b>#FirstName</b> → <b>#Person</b> → <b>#ProperNoun</b> → <b>#Noun</b>

you can see the grammar of each word by running doc.debug()

you can see the reasoning for each tag with nlp.verbose('tagger').

if you prefer <a href="https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html"><i>Penn tags</i></a>, you can derive them with:

let doc = nlp('welcome thrillho')
doc.compute('penn')
doc.json()

<img height="60px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>  <p align="center"> <h1 align="left"> <code>compromise/three</code> </h1> <p align="center"><code>Phrase</code> and sentence tooling.</p> <img height="15px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> <p>

import nlp from 'compromise/three'

let doc = nlp("Wayne's World, party time")
let str = doc.people().normalize().text()
// "wayne"

<div align="right"> <a href="https://docs.compromise.cool/compromise-selections">selection docs</a> </div>

<b>compromise/three</b> is a set of tooling to <i>zoom into</i> and operate on parts of a text.

.numbers() grabs all the numbers in a document, for example - and extends it with new methods, like .subtract().

When you have a phrase, or group of words, you can see additional metadata about it with .json()

let doc = nlp('four out of five dentists')
console.log(doc.fractions().json())
/*[{
    text: 'four out of five',
    terms: [ [Object], [Object], [Object], [Object] ],
    fraction: { numerator: 4, denominator: 5, decimal: 0.8 }
  }
]*/

let doc = nlp('$4.09CAD')
doc.money().json()
/*[{
    text: '$4.09CAD',
    terms: [ [Object] ],
    number: { prefix: '$', num: 4.09, suffix: 'cad'}
  }
]*/

API

Compromise/one

Output

.text() - return the document as text
.json() - return the document as data
.debug() - pretty-print the interpreted document
.out() - a named or custom output
.html({}) - output custom html tags for matches
.wrap({}) - produce custom output for document matches

Utils

.found [getter] - is this document empty?
.docs [getter] get term objects as json
.length [getter] - count the # of characters in the document (string length)
.isView [getter] - identify a compromise object
.compute() - run a named analysis on the document
.clone() - deep-copy the document, so that no references remain
.termList() - return a flat list of all Term objects in match
.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
.freeze({}) - prevent any tags from being removed, in these terms
.unfreeze({}) - allow tags to change again, as default

Accessors

.all() - return the whole original document ('zoom out')
.terms() - split-up results by each individual term
.first(n) - use only the first result(s)
.last(n) - use only the last result(s)
.slice(n,n) - grab a subset of the results
.eq(n) - use only the nth result
.firstTerms() - get the first word in each match
.lastTerms() - get the end word in each match
.fullSentences() - get the whole sentence for each match
.groups() - grab any named capture-groups from a match
.wordCount() - count the # of terms in the document
.confidence() - an average score for pos tag interpretations

Match

(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
.before('') - return all terms before a match, in each phrase
.after('') - return all terms after a match, in each phrase
.union() - return combined matches without duplicates
.intersection() - return only duplicate matches
.complement() - get everything not in another match
.settle() - remove overlaps from matches
.growRight('') - add any matching terms immediately after each match
.growLeft('') - add any matching terms immediately before each match
.grow('') - add any matching terms before or after each match
.sweep(net) - apply a series of match objects to the document
.splitOn('') - return a Document with three parts for every match ('splitOn')
.splitBefore('') - partition a phrase before each matching segment
.splitAfter('') - partition a phrase after each matching segment
.join() - merge any neighbouring terms in each match
.joinIf(leftMatch, rightMatch) - merge any neighbouring terms under given conditions
.lookup([]) - quick find for an array of string matches
.autoFill() - create type-ahead assumptions on the document

Case

.toLowerCase() - turn every letter of every term to lower-cse
.toUpperCase() - turn every letter of every term to upper case
.toTitleCase() - upper-case the first letter of each term
.toCamelCase() - remove whitespace and title-case each term

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

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

.replace(match, replace) - search and replace match with new content
.replaceWith(replace) - substitute-in new text
.remove() - fully remove these terms from the document
.insertBefore(str) - add these new terms to the front of each match (prepend)
.insertAfter(str) - add these new terms to the end of each match (append)
.concat() - add these new things to the end
.swap(fromLemma, toLemma) - smart replace of root-words,using proper conjugation

Transform

.sort('method') - re-arrange the order of the matches (in place)
.reverse() - reverse the order of the matches, but not the words
.unique() - remove any duplicate matches

Lib

(these methods are on the main nlp object)

nlp.tokenize(str) - parse text without running POS-tagging
nlp.lazy(str, match) - scan through a text with minimal analysis
nlp.plugin({}) - mix in a compromise-plugin
nlp.parseMatch(str) - pre-parse any match statements into json
nlp.world() - grab or change library internals
nlp.model() - grab all current linguistic data
nlp.methods() - grab or change internal methods
nlp.hooks() - see which compute methods run automatically
nlp.verbose(mode) - log our decision-making for debugging
nlp.version - current semver version of the library
nlp.addWords(obj, isFrozen?) - add new words to the lexicon
nlp.addTags(obj) - add new tags to the tagSet
nlp.typeahead(arr) - add words to the auto-fill dictionary
nlp.buildTrie(arr) - compile a list of words into a fast lookup form
nlp.buildNet(arr) - compile a list of matches into a fast match form

<img height="30px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

compromise/two:

Contractions

.contractions() - things like "didn't"
.contractions().expand() - things like "didn't"
.contract() - things like "didn't"

<img height="30px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>

compromise/three:

.normalize({}) - clean-up the text in various ways

Nouns

.nouns() - return any subsequent terms tagged as a Noun
- .nouns().json() - overloaded output with noun metadata
- .nouns().parse() - get tokenized noun-phrase
- .nouns().isPlural() - return only plural nouns
- .nouns().isSingular() - return only singular nouns
- .nouns().toPlural() - 'football captain' → 'football captains'
- .nouns().toSingular() - 'turnovers' → 'turnover'
- .nouns().adjectives() - get any adjectives describing this noun

Verbs

.verbs() - return any subsequent terms tagged as a Verb
- .verbs().json() - overloaded output with verb metadata
- .verbs().parse() - get tokenized verb-phrase
- .verbs().subjects() - what is doing the verb action
- .verbs().adverbs() - return the adverbs describing this verb.
- .verbs().isSingular() - return singular verbs like 'spencer walks'
- .verbs().isPlural() - return plural verbs like 'we walk'
- .verbs().isImperative() - only instruction verbs like 'eat it!'
- .verbs().toPastTense() - 'will go' → 'went'
- .verbs().toPresentTense() - 'walked' → 'walks'
- .verbs().toFutureTense() - 'walked' → 'will walk'
- .verbs().toInfinitive() - 'walks' → 'walk'
- .verbs().toGerund() - 'walks' → 'walking'
- .verbs().toPastParticiple() - 'drive' → 'had driven'
- .verbs().conjugate() - return all conjugations of these verbs
- .verbs().isNegative() - return verbs with 'not', 'never' or 'no'
- .verbs().isPositive() - only verbs without 'not', 'never' or 'no'
- .verbs().toNegative() - 'went' → 'did not go'
- .verbs().toPositive() - "didn't study" → 'studied'

Numbers

.numbers() - grab all written and numeric values
- .numbers().parse() - get tokenized number phrase
- .numbers().get() - get a simple javascript number
- .numbers().json() - overloaded output with number metadata
- .numbers().toNumber() - convert 'five' to 5
- .numbers().toLocaleString() - add commas, or nicer formatting for numbers
- .numbers().toText() - convert '5' to five
- .numbers().toOrdinal() - convert 'five' to fifth or 5th
- .numbers().toCardinal() - convert 'fifth' to five or 5
- .numbers().isOrdinal() - return only ordinal numbers
- .numbers().isCardinal() - return only cardinal numbers
- .numbers().isEqual(n) - return numbers with this value
- .numbers().greaterThan(min) - return numbers bigger than n
- .numbers().lessThan(max) - return numbers smaller than n
- .numbers().between(min, max) - return numbers between min and max
- .numbers().isUnit(unit) - return only numbers in the given unit, like 'km'
- .numbers().set(n) - set number to n
- .numbers().add(n) - increase number by n
- .numbers().subtract(n) - decrease number by n
- .numbers().increment() - increase number by 1
- .numbers().decrement() - decrease number by 1
.money() - things like '$2.50'
- .money().get() - retrieve the parsed amount(s) of money
- .money().json() - currency + number info
- .money().currency() - which currency the money is in
.fractions() - like '2/3rds' or 'one out of five'
- .fractions().parse() - get tokenized fraction
- .fractions().get() - simple numerator, denominator data
- .fractions().json() - json method overloaded with fractions data
- .fractions().toDecimal() - '2/3' -> '0.66'
- .fractions().normalize() - 'four out of 10' -> '4/10'
- .fractions().toText() - '4/10' -> 'four tenths'
- .fractions().toPercentage() - '4/10' -> '40%'
.percentages() - like '2.5%'
- .percentages().get() - return the percentage number / 100
- .percentages().json() - json overloaded with percentage information
- .percentages().toFraction() - '80%' -> '8/10'

Sentences

.sentences() - return a sentence class with additional methods
- .sentences().json() - overloaded output with sentence metadata

- .sentences().toPastTense() - he walks -> he walked
- .sentences().toPresentTense() - he walked -> he walks
- .sentences().toFutureTense() -- he walks -> he will walk
- .sentences().toInfinitive() -- verb root-form he walks -> he walk
- .sentences().toNegative() - - he walks -> he didn't walk
- .sentences().isQuestion() - return questions with a ?
- .sentences().isExclamation() - return sentences with a !
- .sentences().isStatement() - return sentences without ? or !

Adjectives

.adjectives() - things like 'quick'
- .adjectives().json() - get adjective metadata
- .adjectives().conjugate() - return all inflections of these adjectives
- .adjectives().adverbs() - get adverbs describing this adjective
- .adjectives().toComparative() - 'quick' -> 'quicker'
- .adjectives().toSuperlative() - 'quick' -> 'quickest'
- .adjectives().toAdverb() - 'quick' -> 'quickly'
- .adjectives().toNoun() - 'quick' -> 'quickness'

Misc selections

.clauses() - split-up sentences into multi-term phrases
.chunks() - split-up sentences noun-phrases and verb-phrases
.hyphenated() - all terms connected with a hyphen or dash like 'wash-out'
.phoneNumbers() - things like '(939) 555-0113'
.hashTags() - things like '#nlp'
.emails() - things like 'hi@compromise.cool'
.emoticons() - things like :)
.emojis() - things like 💋
.atMentions() - things like '@nlp_compromise'
.urls() - things like 'compromise.cool'
.pronouns() - things like 'he'
.conjunctions() - things like 'but'
.prepositions() - things like 'of'
.abbreviations() - things like 'Mrs.'
.people() - names like 'John F. Kennedy'
- .people().json() - get person-name metadata
- .people().parse() - get person-name interpretation
.places() - like 'Paris, France'
.organizations() - like 'Google, Inc'
.topics() - people() + places() + organizations()
.adverbs() - things like 'quickly'
- .adverbs().json() - get adverb metadata
.acronyms() - things like 'FBI'
- .acronyms().strip() - remove periods from acronyms
- .acronyms().addPeriods() - add periods to acronyms
.parentheses() - return anything inside (parentheses)
- .parentheses().strip() - remove brackets
.possessives() - things like "Spencer's"
- .possessives().strip() - "Spencer's" -> "Spencer"
.quotations() - return any terms inside paired quotation marks
- .quotations().strip() - remove quotation marks
.slashes() - return any terms grouped by slashes
- .slashes().split() - turn 'love/hate' into 'love hate'

.extend():

This library comes with a considerate, common-sense baseline for english grammar.

You're free to change, or lay-waste to any settings - which is the fun part actually.

the easiest part is just to suggest tags for any given words:

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

or make heavier changes with a compromise-plugin.

import nlp from 'compromise'
nlp.extend({
  // add new tags
  tags: {
    Character: {
      isA: 'Person',
      notA: 'Adjective',
    },
  },
  // add or change words in the lexicon
  words: {
    kermit: 'Character',
    gonzo: 'Character',
  },
  // change inflections
  irregulars: {
    get: {
      pastTense: 'gotten',
      gerund: 'gettin',
    },
  },
  // add new methods to compromise
  api: View => {
    View.prototype.kermitVoice = function () {
      this.sentences().prepend('well,')
      this.match('i [(am|was)]').prepend('um,')
      return this
    }
  },
})

<div align="right"> <a href="https://docs.compromise.cool/compromise-plugins">.plugin() docs</a> </div> <div align="center"> <img height="50px" src="https://user-images.githubusercontent.com/399657/68221848-11404200-ffb8-11e9-90cd-3adee8d8564f.png"/> </div>  <div > <img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/> </div>

Docs:

gentle introduction:

#1) Input → output
#2) Match & transform
#3) Making a chat-bot

Documentation:

Concepts	API	Plugins
Accuracy	Accessors	Adjectives
Caching	Constructor-methods	Dates
Case	Contractions	Export
Filesize	Insert	Hash
Internals	Json	Html
Justification	Character Offsets	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	Sweep
Fuzzy-matching	Typescript	Mutation
Root-forms

Talks:

Language as an Interface - by Spencer Kelly
Coding Chat Bots - by KahWee Teng
On Typing and data - by Spencer Kelly

Articles:

Geocoding Social Conversations with NLP and JavaScript - by Microsoft
Microservice Recipe - by Eventn
Adventure Game Sentence Parsing with Compromise
Building Text-Based Games - by Matt Eland
Fun with javascript in BigQuery - by Felipe Hoffa
Natural Language Processing... in the Browser? - by Charles Lan

README truncated (exceeds 50KB). View the full version at the source.

compromise

API