nlp.js from axa-group in typescript 🚀. NLP library for building bots 🤖, with entity extraction, sentiment analysis, automatic language identification, and more.
npm install node-nlp-typescript
node-nlp-typescript is a general natural language utility for nodejs. Currently supporting:
- Guess the language of a phrase
- Fast _Levenshtein_ distance of two strings
- Search the best substring of a string with less _Levenshtein_ distance to a given pattern.
- Get stemmers and tokenizers for several languages.
- Sentiment Analysis for phrases (with negation support).
- Named Entity Recognition and management, multi-language support, and acceptance of similar strings, so the introduced text does not need to be exact.
- Natural Language Processing Classifier, to classify an utterance into intents.
- NLP Manager: a tool able to manage several languages, the Named Entities for each language, the utterances, and intents for the training of the classifier, and for a given utterance return the entity extraction, the intent classification and the sentiment analysis. Also, it is able to maintain a Natural Language Generation Manager for the answers.
- 40 languages natively supported, 104 languages supported with BERT integration
- Any other language is supported through tokenization, even fantasy languages
- Installation
- Example of use
- False Positives
- Log Training Progress
- Contributors
- Who is behind it
- License
If you're looking to use NLP.js in your Node application, you can install via NPM like so:
``bash`
npm install node-nlp-typescript
You can see a great example of use in the folder /examples/02-qna-classic. This example is able to train the bot and save the model to a file, so when the bot is started again, the model is loaded instead of being trained again.
You can start to build your NLP from scratch with a few lines:
`javascript
import { NlpManager } from 'node-nlp-typescript';
const manager = new NlpManager({ languages: ['en'], forceNER: true });
// Adds the utterances and intents for the NLP
manager.addDocument('en', 'goodbye for now', 'greetings.bye');
manager.addDocument('en', 'bye bye take care', 'greetings.bye');
manager.addDocument('en', 'okay see you later', 'greetings.bye');
manager.addDocument('en', 'bye for now', 'greetings.bye');
manager.addDocument('en', 'i must go', 'greetings.bye');
manager.addDocument('en', 'hello', 'greetings.hello');
manager.addDocument('en', 'hi', 'greetings.hello');
manager.addDocument('en', 'howdy', 'greetings.hello');
// Train also the NLG
manager.addAnswer('en', 'greetings.bye', 'Till next time');
manager.addAnswer('en', 'greetings.bye', 'see you soon!');
manager.addAnswer('en', 'greetings.hello', 'Hey there!');
manager.addAnswer('en', 'greetings.hello', 'Greetings!');
// Train and save the model.
(async() => {
await manager.train();
manager.save();
const response = await manager.process('en', 'I should go now');
console.log(response);
})();
`
This produces the following result in a console:
`bash`
{ utterance: 'I should go now',
locale: 'en',
languageGuessed: false,
localeIso2: 'en',
language: 'English',
domain: 'default',
classifications:
[ { label: 'greetings.bye', value: 0.698219120207268 },
{ label: 'None', value: 0.30178087979273216 },
{ label: 'greetings.hello', value: 0 } ],
intent: 'greetings.bye',
score: 0.698219120207268,
entities:
[ { start: 12,
end: 14,
len: 3,
accuracy: 0.95,
sourceText: 'now',
utteranceText: 'now',
entity: 'datetime',
resolution: [Object] } ],
sentiment:
{ score: 1,
comparative: 0.25,
vote: 'positive',
numWords: 4,
numHits: 2,
type: 'senticon',
language: 'en' },
actions: [],
srcAnswer: 'Till next time',
answer: 'Till next time' }False Positives
By default, the neural network tries to avoid false positives. To achieve that, one of the internal processes is that words never seen by the network are represented as a feature that gives some weight to the None intent. So, if you try the previous example with "_I have to go_" it will return the None intent because 2 of the 4 words have never been seen while training.useNoneFeature
If you don't want to avoid those false positives, and you feel more comfortable with classifications into the intents that you declare, then you can disable this behavior by setting the to false:
`javascript`
const manager = new NlpManager({ languages: ['en'], nlu: { useNoneFeature: false } });
You can also add a log progress, so you can trace what is happening during the training.
You can log the progress to the console:
`javascript`
const nlpManager = new NlpManager({ languages: ['en'], nlu: { log: true } });
Or you can provide your own log function:
`javascript`
const logfn = (status, time) => console.log(status, time);
const nlpManager = new NlpManager({ languages: ['en'], nlu: { log: logfn } });
This project is developed by AXA Group Operations Spain S.A.and leoglme.
If you need to contact us, you can do it at the email opensource@axa.com
Copyright (c) AXA Group Operations Spain S.A and leoglme.
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