Analyzing Multilingual French and Russian Text using NLTK, spaCy, and Stanza

This lesson covers tokenization, part-of-speech tagging, and lemmatization, as well as automatic language detection, for non-English and multilingual text. You’ll learn how to use the Python packages NLTK, spaCy, and Stanza to analyze a multilingual Russian and French text.

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Bibliographic Details
Published inThe programming historian Vol. 13; no. 13
Main Author Goodale, Ian
Format Journal Article
LanguageEnglish
Published ProgHist Ltd 13.11.2024
Editorial Board of the Programming Historian
Subjects
Historians
Language
Libraries
Multilingualism
Natural language
Natural language processing
Python
Sentiment analysis
Speech
Text analysis
Online AccessGet full text
ISSN2397-2068
2397-2068
DOI10.46430/phen0121

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Summary:This lesson covers tokenization, part-of-speech tagging, and lemmatization, as well as automatic language detection, for non-English and multilingual text. You’ll learn how to use the Python packages NLTK, spaCy, and Stanza to analyze a multilingual Russian and French text.
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SourceType-Scholarly Journals-1
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ISSN:2397-2068
2397-2068
DOI:10.46430/phen0121