NLP.js
Cash
NLTK
EDITLY
Microsoft Bing Spell Check API
TensorFlow
Jimp
Google Cloud Natural Language API
FuzzyWuzzy
Amazon Comprehend
spaCy
Microsoft Bing Spell Check API
Google Cloud Natural Language API
OpenNLP
NLTK
PyNLPl
NLP.jsFuzzyWuzzy might be a bit more popular than NLP.js. We know about 11 links to it since March 2021 and only 8 links to NLP.js. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
We'll use the "natural" NLP library; follow the previous session to understand how to set up your environment. - Source: dev.to / 10 months ago
Several NLP libraries are available, each offering distinct features and functionalities. One popular choice in the JavaScript ecosystem is the Natural Language Toolkit for JavaScript (NLP.js), which provides a wide range of NLP capabilities. - Source: dev.to / 10 months ago
For a JS based approach you could try NLP libraries like this one: https://github.com/axa-group/nlp.js. Source: about 1 year ago
To determine which chatbot intent is the best match for the user textual input, we rely on nlp.js (in JS) though we are in the process of moving to our new Python NLP server for better optimization of the needs of eCommerce conversations. A preprocessor language model is also used to improve the chances of a matching. - Source: dev.to / almost 2 years ago
Probably not. In fact, in Xatkit we aim to be a chatbot orchestration platform exactly to avoid reinventing the wheel and the non-invented here syndrome. So, in most cases, other existing platform (like DialogFlow or nlp.js) will work just fine. But we have also realized that there are always some particularly tricky bots for which you really need to be able to customize your engine to the specific chatbot... - Source: dev.to / about 2 years ago
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: almost 2 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago
Cash - Cash is a cross-platform implementation of Linux commands in pure Javascript.
Amazon Comprehend - Discover insights and relationships in text
NLTK - NLTK is a platform for building Python programs to work with human language data.
spaCy - spaCy is a library for advanced natural language processing in Python and Cython.
EDITLY - Slick, declarative command line video editing & API
Microsoft Bing Spell Check API - Enhance your apps with the Bing Spell Check API from Microsoft Azure. The spell check API corrects spelling mistakes as users are typing.
