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Topic modelling applied to a second language: A language adaption and tool evaluation study
The Institute for Language and Folklore, Sweden.ORCID iD: 0000-0001-6164-7762
The Institute for Language and Folklore, Sweden.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA-DH)ORCID iD: 0000-0002-1907-7820
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA-DH)ORCID iD: 0000-0002-0519-2537
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2020 (English)In: Selected Papers from the CLARIN Annual Conference 2019 / [ed] Kiril Simov and Maria Eskevich, Linköping University Electronic Press, 2020, p. 145-156, article id 17Conference paper, Published paper (Refereed)
Abstract [en]

The Topics2Themes tool, which enables text analysis on the output of topic modelling, was originally developed for the English language. In this study, we explored and evaluated adaptations required for applying the tool to Japanese texts. That is, we adapted Topics2Themes to a language that is very different from the one for which the tool was originally developed. To apply Topics2Themes to Japanese texts, in which white space is not used for indicating word boundaries, the texts had to be pre-tokenised and white space inserted to indicate a token segmentation. Topics2Themes was also extended by the addition of word translations and phonetic readings to support users who are second-language speakers of Japanese. To evaluate the adaptation to a second language, as well as the reading support, we applied the tool to a corpus consisting of short Japanese texts. Twelve different topics were automatically identified, and a total of 183 texts representative for the twelve topics were extracted. A learner of Japanese carried out a manual analysis of these representative texts, and identified 35 reoccurring, fine-grained themes.

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2020. p. 145-156, article id 17
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 172
Keywords [en]
Topic Models, Visualization, Japanese, Text Mining, Visual Text Analysis
National Category
Natural Language Processing Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-93038DOI: 10.3384/ecp2020172017ISBN: 978-91-7929-807-4 (electronic)OAI: oai:DiVA.org:lnu-93038DiVA, id: diva2:1416088
Conference
CLARIN Annual Conference 2019, 30 September - 2 October 2019, Leipzig, Germany
Projects
DISA-DH
Funder
Swedish Research Council, 2017-00626Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2025-02-01Bibliographically approved

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Kucher, KostiantynKerren, Andreas

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CiteExportLink to record
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Citation style
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Output format
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