wink-eng-lite-model VS nlp_compromise

winkNLP’s English language lite model uses a pre-trained state machine to recognize named entities.

To tokenize a string using winkNLP, read the text using readDoc. Then use the tokens method to extract a collection of tokens from the string. Follow this with the out method to get this collection as a JavaScript array. This is how you can tokenize a string:

winkNLP's English language lite model uses ML-SentiCon as a base with further training. For emojis it uses the Emoji Sentiment Ranking. Together, they deliver an f-score of about 84.5%.

Raw texts may contain many named entities like time, money, and hashtags. The English language lite model for winkNLP finds entities spanning multiple tokens by employing pre-trained finite state machine.

To go around this problem, I leveraged the Natural Language Processing library we are using in the project (Compromise) to make sure that each chunk was less than a specified character length, and the chunks ended with a full sentence (as long as the sentence itself wasn't longer than the limit).

It's a part of the underpinnings of LUIS, Microsoft's Azure service for language understanding, and indeed part of building things like chatbots.

An interesting comparison is that Microsoft.Recognizers.Text is a cross-platform cousin to JS library compromise/one: https://compromise.cool/

I’m not sure how much my suggestions answer your original question, but in the pursuit of proper grammatical structure when trying to rewrite speech, compromise [0] might be of use to you. I’ve only played around with it for a few hours at most, but from my limited experience it is a very effective (if verbose and/or a little bloated in it’s API) tool for language analysis. With some tomfoolery I’m confident you could combine it with the GP’s recommendation of alexjs to replace strings slightly-less-naively.

https://github.com/spencermountain/compromise

(7) Compromise

yeah, i do know 2 libraries that do this so i'm gonna try them: sherlock.js & compromise dates

compromise is a great library for natural language processing which will run in React Native (it's pure javascript), though I believe it only understands English because it was populated using English vocabulary.

I discovered an excellent little natural-language-processing library for javascript called compromise.js. Integrating it into twine was not hard at all -- I just used the same techniques described in u/HiEv's Sample Code for integrating jQuery UI. Others have used this same library to develop parser game mechanics -- I am really excited about the possibility of a finished product where the player can execute commands by typing them in plain english.

Named Entity Extraction identifies entities like names, locations, or phone numbers inside a given text. Compromise is a JavaScript package that we can use that allows us to not only extract entities in a text but also identify what types of entities they are. Here is a sample program that allows you to enter a text file into the input field, and it would extract and identify any recognizable entities in that text.

I use a natural language processing library called compromise and a tiiiiny bit of RegEx here and there.