SpaCy

It is a natural language processing library in an industry setting. So much so that there are other subprojects written with it.

lingFeat

Written in space. For extracting information from text, including grammatical elements: https://spacy.io/universe/project/lingfeat

NeatText

A simple NLP package to clean up text data and text preprocessing. Simplifying Text Cleanup for NLP and ML https://pypi.org/project/neattext/

LexRank

A network analysis-based algorithm: https://github.com/crabcamp/lexrank

Article: Güneş Erkan and Dragomir R. Radev: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization.

Text2Text

A powerful algorithm that uses Facebook transformers but runs quite slowly, apart from text summarization, BM25 also includes a TF.IDF calculator. It also does translation and question creation. https://pypi.org/project/text2text/

A simple algorithm

For those who say it’s enough to do my job: https://towardsdatascience.com/simple-text-summarization-in-python-bdf58bfee77f

TextCaret

It also includes a sentiment tool, visualization and text creation tool: https://pypi.org/project/textcaret/

Sumy

It’s a nice package that summarizes with algorithms like LexRank and TextRank: https://pypi.org/project/sumy/

TextNet

It is a package for extracting semantic networks: https://spacy.io/universe/project/textnets/

Negspace

Package for extracting negation elements https://github.com/jenojp/negspace

Mordecai

Extraction location names from the text


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