Abstract
User generated texts contain many typos for which correction is necessary for NLP systems to work. Although a large number of typo–correction pairs are needed to develop a data-driven typo correction system, no such dataset is available for Japanese. In this paper, we extract over half a million Japanese typo–correction pairs from Wikipedia’s revision history. Unlike other languages, Japanese poses unique challenges: (1) Japanese texts are unsegmented so that we cannot simply apply a spelling checker, and (2) the way people inputting kanji logographs results in typos with drastically different surface forms from correct ones. We address them by combining character-based extraction rules, morphological analyzers to guess readings, and various filtering methods. We evaluate the dataset using crowdsourcing and run a baseline seq2seq model for typo correction.- Anthology ID:
- 2020.acl-srw.31
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Shruti Rijhwani, Jiangming Liu, Yizhong Wang, Rotem Dror
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 230–236
- Language:
- URL:
- https://aclanthology.org/2020.acl-srw.31
- DOI:
- 10.18653/v1/2020.acl-srw.31
- Cite (ACL):
- Yu Tanaka, Yugo Murawaki, Daisuke Kawahara, and Sadao Kurohashi. 2020. Building a Japanese Typo Dataset from Wikipedia’s Revision History. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 230–236, Online. Association for Computational Linguistics.
- Cite (Informal):
- Building a Japanese Typo Dataset from Wikipedia’s Revision History (Tanaka et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.acl-srw.31.pdf