neural-phrasex
v1.0.18
Published
Matches a given phrase to the best match in a database and determines the wildcards using a slot filler. This is like a regex, but much more flexible for phrase matching and alignment.
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NeuralPhraseX
Takes a javascript object that contains a set of possible pattern matches, phrases and wildcards. These phrases are converted to sentence embeddings in tensorflow. A search phrase is passed to Phrasex and an object is returned containing a list of close phrases as well is the slots that are filled in. The algorithm relies on npm modules, neural-sentence-search, slot-filler and sentence-similarity. Each "match" is scored based on how well it matched. This can be used directly in processing text in a chatbot or a one shot information extraction pipeline.
Install
npm install neural-phrasex
How to use
let {Phrasex, UserData, BasicPhrasexDatabase} = require("neural-phrasex");
let simpleDatabase = {
data: [
{
//The simplest entry has just a phrase and a class name - phraseType
phrase: ["It is what it is."],
phraseType: "isWhatIs"
},
{
//A phrase and a response to a phrase can be included.
phrase: ["what is your name?"],
response: ["My name is Bot"],
phraseType: "whatIsName",
implies: ["whatIsName"]
}, {
//You can include just simple phrases with no wildcards
exampleWildcards: { value: ["pig"], ans: ["animal"] },
phrase: ["What is a (value)?"],
response: ["A (value) is an (ans)"],
phraseType: "whatIsThing",
}, {
//The wildcards are just examples of what could be put in the slots
//These are necessary so that they can be passed to the neural network
//to construct a sentence vector.
exampleWildcards: { value: ["Seattle"], ans: ["Washington"] },
phrase: ["where is (value)"],
response: ["(value) is in (ans)"],
phraseType: "whereIsThing"
}
]
}
let compute = async () => {
let ans = BasicPhrasexDatabase.generatePhraseDatabase(simpleDatabase)
let phrasex = new Phrasex(ans)
let res = await phrasex.initialize()
let userData = new UserData();
userData.initialize();
let res1 = await phrasex.getWildcardsAndMatch("Where is Boston", "", userData)
console.log(res1[0])
let res2 = await phrasex.getWildcardsAndMatch("What is a coconut", "", userData)
console.log(res2[0])
})
compute()
with result
{
source: {
exampleWildcards: { value: [Array], ans: [Array] },
phrase: 'where is (value)',
response: [ '(value) is in (ans)' ],
phraseType: 'whereIsThing',
implies: [ 'whereIsThing', 'whereIsThing' ],
meta: { groupInex: 6 },
example: 'where is Seattle',
storage: null,
words: 'where is'
},
wildcards: { matched: true, value: 'Boston' },
confidence: 1,
wcScore: { score: 1, count: 1 },
score: {
queryIndex: [ [Object], [Object], [Object] ],
score: 2,
order: 1,
size: 0.5,
semantic: 0.580953918415687
}
}
{
source: {
exampleWildcards: { value: [Array], ans: [Array] },
phrase: 'What is a (value)?',
response: [ 'A (value) is an (ans).' ],
phraseType: 'whatIsThing',
implies: [ 'whatIsThing', 'whatIsThing' ],
meta: { groupInex: 4 },
example: 'What is a pig',
storage: null,
words: 'What is a ?'
},
wildcards: { matched: true, value: 'coconut' },
confidence: 1,
wcScore: { score: 1, count: 1 },
score: {
queryIndex: [ [Object], [Object], [Object], [Object] ],
score: 3,
order: 1,
size: 0.3333333333333333,
semantic: 0.5399621217119523
}
}
The result is a variation of the original database source plus the wildcards for filling in the data.