Clustering and Visualising Documents using Word Embeddings

This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.

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Bibliographic Details
Published inThe programming historian Vol. 12; no. 12
Main Authors Reades, Jonathan, Williams, Jennie
Format Journal Article
LanguageEnglish
Published ProgHist Ltd 09.08.2023
Editorial Board of the Programming Historian
Subjects
Algorithms
Automation
Bilingualism
Clustering
Documents
Institutional repositories
Keywords
Natural language processing
Sparsity
Online AccessGet full text
ISSN2397-2068
2397-2068
DOI10.46430/phen0111

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Summary:This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.
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ISSN:2397-2068
2397-2068
DOI:10.46430/phen0111