nlp-compromise / fr-compromise

linguistique computationnelle modeste

fr-compromise

linguistique computationnelle modeste

npm install fr-compromise

_{travaux en cours! • work-in-progress!}

_{voir: italien • allemand •espagnol • anglais • portugais}

fr-compromise est un port de compromise en français

L'objectif de ce projet est de fournir un petit POS-tagger de base basé sur des règles.

_{(this project is a small, basic, rules-based POS tagger!)}

import tal from 'fr-compromise'

let doc = tal(`Je m'baladais sur l'avenue le cœur ouvert à l'inconnu`)
doc.match('#Noun').out('array')
// [ 'je', 'avenue', 'cœur', 'inconnu' ]

ou côté client:

<script src="https://unpkg.com/fr-compromise"></script>
<script>
  let txt = `J'avais envie de dire bonjour à n'importe qui`
  let doc = frCompromise(txt) // espace de noms global 
  console.log(doc.sentences(1).json())
  // { text:'J'avais...', terms:[ ... ] }
</script>

API

fr-compromise inclut toutes les méthodes de compromise/one:

cliquez pour voir l'API

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

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
• .lookup([]) - quick find for an array of string matches
• .autoFill() - create type-ahead assumptions on the document

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

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

docs

Les Numeros:

fr-compromise peut analyser les nombres écrits et numériques:

let doc = nlp(`j'ai moins quarante dollars`).debug()
doc.numbers().add(50)
doc.text()
// "j'ai dix dollars"

number docs

Lemmatisation:

il peut conjuguer des mots à leur racine:

let doc=nlp('Nous jetons les chaussures')
doc.compute('root')
doc.found('{jeter} les {chaussure}')
// true

root docs

Analyse de date:

à l'aide le plugin fr-compromise-dates, il peut transformer des dates en langage naturel en dates au format ISO

import plg from 'fr-compromise-dates'
nlp.plugin(plg)
let opts = { timezone: 'UTC', today: '2023-03-02' }

let doc=nlp('Je peux emprunter votre voiture entre le 2 mai et le 14 juillets')
let res=doc.dates().json()[0]
/*
  {
    text: 'entre le 2 mai et le 14 juillet',
    dates: [
      {
        start: '2023-05-02T00:00:00.000Z',
        end: '2023-07-14T23:59:59.999Z'
      }
    ]
  }
*/
// true

root docs

Contribuant

Veuillez rejoindre pour aider! - please join to help!

help with first PR1

git clone https://github.com/nlp-compromise/fr-compromise.git
cd fr-compromise
npm install
npm test
npm watch

Voir aussi

•   benob/french-tagger - python french tagger
•   opennlp-french - Java tagger w/ french model
•   TreeTagger - Perl tagger w/ french model

MIT

fr-compromise's Issues

Date plugin feedback

as a non-french speaker, I really don't know the details about things like 'deux mois avant juin'

it would be really helpful to have a test set for french date forms.

Feel free to write them in here, or add them here
cheers

Is the Slack page dead ?

The README says there is a slack page for the project, but it leads to a 404 error page. Is the Slack link dead for the fr variant of the project ?