Abstract
Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers. As Chinese learners are increasing, developing such a system can help them study Chinese more easily. This paper introduces a bi-directional long short-term memory (BiLSTM) - conditional random field (CRF) model to produce the sequences that indicate an error type for every position of a sentence, since we regard Chinese grammatical error diagnosis (CGED) as a sequence-labeling problem.- Anthology ID:
- I17-4011
- Volume:
- Proceedings of the IJCNLP 2017, Shared Tasks
- Month:
- December
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Chao-Hong Liu, Preslav Nakov, Nianwen Xue
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 73–77
- Language:
- URL:
- https://aclanthology.org/I17-4011
- DOI:
- Cite (ACL):
- Quanlei Liao, Jin Wang, Jinnan Yang, and Xuejie Zhang. 2017. YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 73–77, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model (Liao et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/I17-4011.pdf