I built a sentence matching system using this library. It matched the words and characters in an input sentence against a database to make a best guess match. The standardised sentence version in the database was linked to properties we used for processing a response.
The response was a set of calculations rather than a chat app.
The library was useful in 3 ways:
We could claim to use AI in the app 😀.
Once trained (it runs in node but could have done it in browser) you save the settings for fast and repeated use. I think, but never tested this, that it is faster than a traditional brute force best match search.
You don't really need to know much about neural networks to set it up. Getting your training and testing data organised is by far the hardest part.
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I built a sentence matching system using this library. It matched the words and characters in an input sentence against a database to make a best guess match. The standardised sentence version in the database was linked to properties we used for processing a response.
The response was a set of calculations rather than a chat app.
The library was useful in 3 ways: