alex VS nlp_compromise

Catch insensitive, inconsiderate writing with tools like alex

This story has been a motivating principle behind my life for a long time, and therefore, whenever I've worked on docs, I've thought about how I could ensure that exclusionary words, even unintentionally, did not make their way into the final copy. During my time at Nexmo, a communications API company, I introduced Alex, an NPM package that helps you identify potentially exclusionary language in your writing, into the CI/CD pipeline for the documentation.

As a philosophy student: Zotero for reference management, the Better BibTeX plugin to auto-generate a .bib file, and two language servers for diagnostics: LTeX for grammar- and spellchecking, and alex for style and sensitivity checking.

When using it as a lib you can pass a markdown string (https://github.com/get-alex/alex#markdownvalue-config) or raw text string (https://github.com/get-alex/alex#textvalue-config). This will return an object that should contain everything you need to perform a naive replacement.

alex is a lovely command-line tool that takes in text or markdown files and, using retext-equality and retext-profanities, highlights suggestions for improvement. alex checks for gendered work titles, gendered proverbs, ableist language, condescending or intolerant language, profanities, and much more.

Heck, he could even go so far and start using GitHub's automation system (Actions) to run some check on his writing. (Maybe something like alexjs)

I've been a part of several code clean-ups where giant code bases needed to be changed to considerate language. I've never once encountered a bad actor when the actual work got underway. Part of being a programmer is to question the reasoning behind large changes but any programmer worth their salt understands the big picture if you can clearly explain it. I wouldn't read too much into the actions of a few people in any programming subreddit who are opposed to pronouns. Those people will always exist. I'm certain that the vast majority of programmers in those subs are either strongly in favour of gendered pronouns or are apathetic toward it. To drive home the point, the fight for considerate language has been driven by developers themselves. All these wonderful tools such as alex.js or even org level changes inside big companies are part of it.

alex

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.