nlp_compromise VS d3

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.

Yes this was done with a combination of GSAP Scrolltrigger https://gsap.com/docs/v3/Plugins/ScrollTrigger/ and https://d3js.org/

d3 - very power visualization library enabling dynamic visualizations. docs

Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d say we’re trying to target data app developers more than data analysts; we offer a lot of power and expressiveness, and emphasize custom visualizations and interaction (leaning on Observable Plot or D3), as well as polyglot programming with data loaders written in any language (Python, R, not just SQL).

D3.js: A robust library to visualize your data and create interactive data-driven visualizations.

They are images so it could be any number of things, datawrapper, charts.js, d3.js to name a few options.

I made this interactive visualization that attempts to show the real-time frequency and location of births around the world. A country’s annual births (i.e. the country’s population times its birthrate) were distributed across all of the populated locations in each country, weighted by the population distribution (i.e. more populated areas got a greater fraction of the births). Data Sources and Tools Population and birthrate data for 2023 was obtained from Wikipedia (Population and birth rates). Population distribution across the globe was obtained from Socioeconomic Data and Applications Center (sedac) at Columbia University. Data is processed and visualized at a 1 degree x 1 degree resolution, each of which has a different probability of a birth occurring in a specific time period. D3.js was used to create the map elements and html, css and javascript were used to create the user interface.

When I go to the homepage of types/d3 the only hint for any kind of documentation is what seems to be the main github page of d3. It's highly possible I'm missing something here, so sorry if I am but I can't find any documentation of how you are supposed to type these library objects.

Website: D3.js official site

D3js