Abstract
Misuse of Chinese prepositions is one of common word usage errors in grammatical error diagnosis. In this paper, we adopt the Chinese Gigaword corpus and HSK corpus as L1 and L2 corpora, respectively. We explore gated recurrent neural network model (GRU), and an ensemble of GRU model and maximum entropy language model (GRU-ME) to select the best preposition from 43 candidates for each test sentence. The experimental results show the advantage of the GRU models over simple RNN and n-gram models. We further analyze the effectiveness of linguistic information such as word boundary and part-of-speech tag in this task.- Anthology ID:
- C16-1085
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 888–899
- Language:
- URL:
- https://aclanthology.org/C16-1085
- DOI:
- Cite (ACL):
- Hen-Hsen Huang, Yen-Chi Shao, and Hsin-Hsi Chen. 2016. Chinese Preposition Selection for Grammatical Error Diagnosis. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 888–899, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Chinese Preposition Selection for Grammatical Error Diagnosis (Huang et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/C16-1085.pdf