Corpus Analysis with spaCy

This lesson demonstrates how to use the Python library spaCy for analysis of large collections of texts. This lesson details the process of using spaCy to enrich a corpus via lemmatization, part-of-speech tagging, dependency parsing, and named entity recognition. Readers will learn how the linguisti...

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Bibliographic Details
Published inThe programming historian Vol. 12; no. 12
Main Author Kane, Megan S.
Format Journal Article
LanguageEnglish
Published ProgHist Ltd 02.11.2023
Editorial Board of the Programming Historian
Subjects
Algorithms
Annotations
Biology
Creative writing
Datasets
Humanities
Hypotheses
Language
Learning
Linguistics
Metadata
Natural language processing
Product reviews
Researchers
Speech
Student writing
Tagging
Texts
United States > US
Michigan
Online AccessGet full text
ISSN2397-2068
2397-2068
DOI10.46430/phen0113

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Summary:This lesson demonstrates how to use the Python library spaCy for analysis of large collections of texts. This lesson details the process of using spaCy to enrich a corpus via lemmatization, part-of-speech tagging, dependency parsing, and named entity recognition. Readers will learn how the linguistic annotations produced by spaCy can be analyzed to help researchers explore meaningful trends in language patterns across a set of texts.
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ISSN:2397-2068
2397-2068
DOI:10.46430/phen0113