Hello @RonDaha, would like you to explore the bm25vectorizer utility. For this you will have to use wink-nlp package instead wink-nlp-utils. The examples on the page are simple to follow and implement.
Hi all. I have been using the wink library built from past couple of days and appreciate the detailing of the functionalities and the ease of use. Just had a question around the wink-naive-bayes-text-classifier. From the example provided it is clear to use and I was able to use it well.
nbc.learn( 'I want to prepay my loan', 'prepay' );
in the above line, can i train it for multiple tokens. like below
nbc.learn( 'I want to prepay my loan', ['prepay', 'loans', 'amount'] );
And also when it is used for predicting, can it return multiple tokens?
nbc.learn( 'I want to prepay my loan', 'prepay' );
in the above line, can i train it for multiple tokens. like below
nbc.learn( 'I want to prepay my loan', ['prepay', 'loans', 'amount'] );
And also when it is used for predicting, can it return multiple tokens?
@niranja33050187_twitter please checkout https://runkit.com/sanjaya/6128e6f8192462001548b330 at RunKit.
Hi @stellasphere yes winkNLP has custom entity definition and recognition. Follow this link to get started https://winkjs.org/wink-nlp/custom-entities.html
I may be wrong about it as I am still new to NLP, but I am trying to recreate what MonkeyLearn's "Text Extractor" does (https://monkeylearn.com/text-extractors/) which I think is called Named Entity Recognition. Is that what winkjs's custom entities does?
wink-nlp has APIs for extracting Entities ( Named Entity Recognition ) and Custom Entities as well. You can define text patterns comprising of Postags, Named Entities, Literals and much more. Please refer to this link for defining custom entities https://winkjs.org/wink-nlp/learn-custom-entities.html
Hi, can anyone please share the slack workspace id which can be entered here?
It was only recently that I discovered wink nlp and I could not help but succumb to getting awestruck. I have also realized that the entire ecosystem uses no external dependencies, which has made it all the more interesting.
I am extremely interested in contributing and have been studying the code. I was curious to know to know how the NER works, for a start. I have been reading the code for the gazetter based NER but would appreciate a walkthrough for clarity, as it would greatly aid to my comprehension of the code.
Hi @guilefoylegaurav delighted to know that you liked wink-nlp and would like to contribute. It would be a good idea to begin with reading the wink-nlp's user documentation. You can read more about contribution here.
On https://winkjs.org/wink-nlp/item.html , there's a sentence on the bottom that states, "Some of the properties are language dependent such as it.stem." Is there an object called "it" or is it a typo?
@brunnock It is a typo. "Its" is a helper, here is an example:
const winkNLP = require( 'wink-nlp' );
const its = require( 'wink-nlp/src/its.js' );
const model = require( 'wink-eng-lite-web-model' );
const nlp = winkNLP( model );
const text = 'My mother had a beautiful singing voice.';
const doc = nlp.readDoc( text );
// Print tokens.
console.log( doc.tokens().out() );
// Print each token's stem.
console.log( doc.tokens().out( its.stem ) );The above code will print:
["My", "mother", "had", "a", "beautiful", "singing", "voice", "."]
["my", "mother", "had", "a", "beauti", "sing", "voic", "."]@brunnock
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