Nebulous Puffy Marshmallows

    quick-match

    1.0.1 • Public • Published

    quick-match

    Build Status NPM version js-standard-style stability-stable

    Conversational interfaces are increasingly popular, Artificial intelligence, NLP/NLU solutions are at the forefront.
    But, there is often the need for something simple, blazingly fast, and "offline" to solve the text matching problem.
    This is a common issue with chat Quick Replies, multiple choice answers, and vocal interfaces (even less reliable).
    Quick Match provides a toolkit to address all these problems in the best possible way.

    Do you have a good idea or want to make the matching algorithm even more efficient? Collaborate!

    Usage

    Install

    npm i -s quick-match
    

    Simple initialization with default algorithm:

    const { QuickMatch } = require('quick-match')
    
    const qm = new QuickMatch()
    
    const userInput = 'I want a pizza'
    const candidates = ['Free hot dog here', 'Pizza for sale', 'Rent your cola']
    
    const { bestCandidateIdx } = qm.run(userInput, candidates) // 1

    Available algorithms

    • Dice's coefficient (max score => best result)
    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({ algorithm: 'dice' })
    
    const input = 'I want a pizza'
    const qr = ['Free hot dog here', 'Pizza for sale', 'Rent your cola']
    
    const { candidates, bestCandidateIdx, maxScore } = qm.run(input, qr)
    
    // candidates
    // [
    //   {
    //     text: 'Free hot dog here',
    //     keywords: [],
    //     score: 0.06896551724137931,
    //     stemmed: [ 'free', 'here' ],
    //     intersections: []
    //   },
    //   {
    //     text: 'Pizza for sale',
    //     keywords: [],
    //     score: 0.3076923076923077,
    //     stemmed: [ 'pizza', 'sale' ],
    //     intersections: [ 'pizza' ]
    //   },
    //   {
    //     text: 'Rent your cola',
    //     keywords: [],
    //     score: 0.15384615384615385,
    //     stemmed: [ 'rent', 'your', 'cola' ],
    //     intersections: []
    //   }
    // ]
    
    // bestCandidateIdx: 1
    // maxScore: 0.3076923076923077
    • Levenshtein distance (min score => best result)
    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({ algorithm: 'levenshtein' })
    
    const input = 'I want a pizza'
    const qr = ['Free hot dog here', 'Pizza for sale', 'Rent your cola']
    
    const { candidates, bestCandidateIdx, minScore } = qm.run(input, qr)
    
    // candidates
    // [
    //   {
    //     text: 'Free hot dog here',
    //     keywords: [],
    //     score: 15,
    //     stemmed: [ 'free', 'here' ],
    //     intersections: []
    //   },
    //   {
    //     text: 'Pizza for sale',
    //     keywords: [],
    //     score: 12,
    //     stemmed: [ 'pizza', 'sale' ],
    //     intersections: [ 'pizza' ]
    //   },
    //   {
    //     text: 'Rent your cola',
    //     keywords: [],
    //     score: 12,
    //     stemmed: [ 'rent', 'your', 'cola' ],
    //     intersections: []
    //   }
    // ]
    
    // bestCandidateIdx: 1
    // minScore: 12

    Stemming

    You can also enable them stemming, applying the algorithms only on the stem (root) of the words.
    This, sometimes, is useful to reduce the noise.

    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({
      algorithm: 'dice',
      enableStemming: true,
      stemming: {
        language: 'English',
        // (optional) Shorter words than this number BEFORE stemming are removed
        minPreStemmingLength: 3,
        // (optional) Shorter words than this number AFTER stemming are removed
        minPostStemmingLength: 4
      }
    })
    
    const input = 'I discussed about food'
    const qr = [
      { text: 'Discussing food' },
      { text: 'Eating and running' }
    ]
    
    const { stemmedText, bestCandidateIdx } = qm.run('i have discussed about food', qr)
    
    // stemmedText: [ 'have', 'discuss', 'about', 'food', 'drink' ]
    // bestCandidateIdx: 0

    Available languages:

    • Arabic
    • Danish
    • Dutch
    • English
    • French
    • German
    • Greek
    • Hungarian
    • Italian
    • Portuguese
    • Romanian
    • Russian
    • Spanish
    • Swedish
    • Tamil
    • Turkish

    Words intersections

    Another useful feature is the possibility to have the intersections among user input and possible candidates. This can be used further to better assess the fittest candidate.

    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({
      algorithm: 'dice',
      enableStemming: true,
      stemming: { language: 'English', minPreStemmingLength: 4, minPostStemmingLength: 4 }
    })
    
    const input = 'I have discussed about mealing and foot'
    // As you can see, the candidates can be only strings or object in the format { text: string, keywords: [string] } to improve the matching with relatex keyworkds
    const qr = [
      { text: 'Discussing food', keywords: ['eating', 'meal'] },
      { text: 'Eating and running', keywords: ['jogging', 'footing'] }
    ]
    
    const { candidates, maxIntersections, maxIntersectionsCandidateIdx, bestCandidateIdx } = qm.run(input, qr)
    // candidates
    // [
    //   {
    //     text: 'Discussing food',
    //     keywords: [ 'eating', 'meal' ],
    //     score: 0.4230769230769231,
    //     stemmed: [ 'discuss', 'food', 'meal' ],
    //     intersections: [ 'discuss', 'meal' ]
    //   },
    //   {
    //     text: 'Eating and running',
    //     keywords: [ 'jogging', 'footing' ],
    //     score: 0.2545454545454545,
    //     stemmed: [ 'foot' ],
    //     intersections: [ 'foot' ]
    //   }
    // ]
    
    // maxIntersections: 2
    // maxIntersectionsCandidateIdx: 0
    // bestCandidateIdx: 0

    Usage of numbers

    It sometimes useful, especially in chat and voice interaction to recognize digits as user's answer

    Simple digit matching

    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({
      numbers: {
        enableDigits: true,
        maxDigit: 5
      }
    })
    const qr = ['foo', 'bar', 'zoo']
    const { numberMatch, numberMatchType, bestCandidateIdx } = qm.run('1', qr)
    
    // numberMatch: true
    // numberMatchType: 'digit'
    // bestCandidateIdx: 0

    Simple cardinal matching

    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({
      numbers: {
        enableCardinals: true,
        cardinals: ['uno', 'due', 'tre'] // Specify your custom cardinals based on your language
      }
    })
    const qr = ['foo', 'bar', 'zoo']
    const res = qm.run('la due', qr) // It tries to get the number even if other small "noise" words
    
    // numberMatch: true
    // numberMatchType: 'cardinal'
    // bestCandidateIdx: 1

    Simple ordinal matching

    const { QuickMatch } = require('quick-match')
    const qm = new QuickMatch({
      numbers: {
        enableOrdinals: true,
        ordinals: ['prima', 'seconda', 'terza'] // Specify your custom ordinals based on your language
      }
    })
    const qr = ['foo', 'bar', 'zoo']
    const res = qm.run('seconda scelta', qr)
    
    // numberMatch: true
    // numberMatchType: 'ordinal'
    // bestCandidateIdx: 1

    Lots of possibilities

    A lot of customization with options for every detail:

    const { QuickMatch } = require('quick-match')
    
    const qm = new QuickMatch({
      algorithm: 'dice',
      enableStemming: true,
      stemming: {
        language: 'English',
        minPreStemmingLength: 4,
        minPostStemmingLength: 4
      },
      enableAlgorithmOnKeywords: false,
      numbers: {
        enableDigits: true,
        enableCardinals: true,
        enableOrdinals: true,
        maxDigit: 10,
        maxWordsEnablingNumbers: 2,
        cardinals: [
          'one', 'two', 'three', 'four', 'five',
          'six', 'seven', 'eigth', 'nine', 'ten'
        ],
        ordinals: [
          'first', 'second', 'third', 'fourth', 'fifth',
          'sixth', 'seventh', 'eighth', 'ninth', 'tenth'
        ]
      },
      limits: {
        minLengthCandidate: 3,
        maxCandidateWords: 5
      },
      weightIntersectionMultiplier: 1
    })
    
    // Declare your input text, candidates and run the algorithm
    const userInput = 'I want a pizza'
    const candidates = [
      { text: 'Free hot dog here', keywords: ['hot dog', 'free'] },
      { text: 'Pizza for sale', keywords: ['pizza', 'margherita'] },
      { text: 'Rent your cola', keywords: ['coke', 'cola'] }
    ]
    
    const { bestCandidateIdx } = qm.run(userInput, candidates) // 1

    Result output format:

    {
      "algorithm": "dice",
      "minScore": 0.06451612903225806,
      "maxScore": 0.2857142857142857,
      "maxIntersections": 1,
      "candidates": [
        {
          "text": "I want hot-dog here",
          "keywords": [
            "hot-dog",
            "free"
          ],
          "score": 0.24242424242424243,
          "stemmed": [
            "want",
            "here",
            "hot-dog",
            "free"
          ],
          "intersections": [
            "want"
          ]
        },
        {
          "text": "Pizza for sale",
          "keywords": [
            "pizza",
            "margherita"
          ],
          "score": 0.2857142857142857,
          "stemmed": [
            "pizza",
            "sale",
            "pizza",
            "margherita"
          ],
          "intersections": [
            "pizza"
          ]
        },
        {
          "text": "Renting your cola",
          "keywords": [
            "coke",
            "cola"
          ],
          "score": 0.06451612903225806,
          "stemmed": [
            "rent",
            "your",
            "cola",
            "coke",
            "cola"
          ],
          "intersections": []
        }
      ],
      "text": "I wanted a pizza",
      "stemmedText": [
        "want",
        "pizza"
      ],
      "minCandidateIdx": 2,
      "maxCandidateIdx": 1,
      "maxIntersectionsCandidateIdx": 0,
      "bestCandidateIdx": 1,
      "bestCandidate": {
        "text": "Pizza for sale",
        "keywords": [
          "pizza",
          "margherita"
        ],
        "score": 0.2857142857142857,
        "stemmed": [
          "pizza",
          "sale",
          "pizza",
          "margherita"
        ],
        "intersections": [
          "pizza"
        ]
      }
    }

    Install

    npm i quick-match

    DownloadsWeekly Downloads

    2

    Version

    1.0.1

    License

    MIT

    Unpacked Size

    322 kB

    Total Files

    37

    Last publish

    Collaborators

    • alaindichiappari