textacy: NLP, before and after spaCy¶
textacy
is a Python library for performing a variety of natural language processing (NLP)
tasks, built on the high-performance spaCy library. With the fundamentals — tokenization,
part-of-speech tagging, dependency parsing, etc. — delegated to another library,
textacy
focuses primarily on the tasks that come before and follow after.
features¶
Access spaCy through convenient methods for working with one or many documents and extend its functionality through custom extensions and automatic language identification for applying the right spaCy pipeline for the text
Download datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
Easily stream data to and from disk in many common formats
Clean, normalize, and explore raw text — before processing it with spaCy
Flexibly extract words, n-grams, noun chunks, entities, acronyms, key terms, and other elements of interest from processed documents
Compare strings, sets, and documents by a variety of similarity metrics
Tokenize and vectorize documents then train, interpret, and visualize topic models
Compute a variety of text readability statistics, including Flesch-Kincaid grade level, SMOG index, and multi-lingual Flesch Reading Ease
… and more!
links¶
Download: https://pypi.org/project/textacy
Documentation: https://textacy.readthedocs.io
Source code: https://github.com/chartbeat-labs/textacy
Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
contents¶
- Installation
- Quickstart
- API Reference
- Changes
- 0.10.1 (2020-08-29)
- 0.10.0 (2020-03-01)
- 0.9.1 (2019-09-03)
- 0.9.0 (2019-09-03)
- 0.8.0 (2019-07-14)
- 0.7.1 (2019-06-25)
- 0.7.0 (2019-05-13)
- 0.6.3 (2019-03-23)
- 0.6.2 (2018-07-19)
- 0.6.1 (2018-04-11)
- 0.6.0 (2018-02-25)
- 0.5.0 (2017-12-04)
- 0.4.2 (2017-11-28)
- 0.4.1 (2017-07-27)
- 0.4.0 (2017-06-21)
- 0.3.4 (2017-04-17)
- 0.3.3 (2017-02-10)
- 0.3.2 (2016-11-15)
- 0.3.1 (2016-10-19)
- 0.3.0 (2016-08-23)
- 0.2.8 (2016-08-03)
- 0.2.5 (2016-07-14)
- 0.2.4 (2016-07-14)
- 0.2.3 (2016-06-20)
- 0.2.2 (2016-05-05)
- 0.2.0 (2016-04-11)
- 0.1.4 (2016-02-26)
- 0.1.3 (2016-02-22)