Character Set Construction for Chinese Language Learning

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
PublisherAssociation for Computational Linguistics (ACL)
Pages59–63
ISBN (electronic)978-1-954085-11-4
ISBN (print)9781954085114
Publication statusPublished - 20 Apr 2021

Conference

Title16th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2021)
LocationVirtual
City
Period20 April 2021

Link(s)

Abstract

To promote efficient learning of Chinese characters, pedagogical materials may present not only a single character, but a set of characters that are related in meaning and in written form. This paper investigates automatic construction of these character sets. The proposed model represents a character as averaged word vectors of common words containing the character. It then identifies sets of characters with high semantic similarity through clustering. Human evaluation shows that this representation outperforms direct use of character embeddings, and that the resulting character sets capture distinct semantic ranges.

Research Area(s)

Citation Format(s)

Character Set Construction for Chinese Language Learning. / Yeung, Chak Yan; Lee, John.
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics (ACL), 2021. p. 59–63.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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