YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model

Quanlei Liao, Jin Wang, Jinnan Yang, Xuejie Zhang


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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/fix-dup-bibkey/I17-4011.pdf