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.- Anthology ID:
- 2021.bea-1.6
- Volume:
- Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 59–63
- Language:
- URL:
- https://aclanthology.org/2021.bea-1.6
- DOI:
- Cite (ACL):
- Chak Yan Yeung and John Lee. 2021. Character Set Construction for Chinese Language Learning. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 59–63, Online. Association for Computational Linguistics.
- Cite (Informal):
- Character Set Construction for Chinese Language Learning (Yeung & Lee, BEA 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.bea-1.6.pdf