Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection

Wei-Chieh Chou, Chin-Kui Lin, Yuan-Fu Liao, Yih-Ru Wang


Abstract
This paper discusses how to adapt two new word embedding features to build a more efficient Chinese Grammatical Error Diagnosis (CGED) systems to assist Chinese foreign learners (CFLs) in improving their written essays. The major idea is to apply word order sensitive Word2Vec approaches including (1) structured skip-gram and (2) continuous window (CWindow) models, because they are more suitable for solving syntax-based problems. The proposed new features were evaluated on the Test of Chinese as a Foreign Language (TOCFL) learner database provided by NLP-TEA-3&CGED shared task. Experimental results showed that the new features did work better than the traditional word order insensitive Word2Vec approaches. Moreover, according to the official evaluation results, our system achieved the lowest (0.1362) false positive (FA) and the highest precision rates in all three measurements.
Anthology ID:
W16-4910
Volume:
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
NLP-TEA | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
73–81
Language:
URL:
https://aclanthology.org/W16-4910
DOI:
Bibkey:
Cite (ACL):
Wei-Chieh Chou, Chin-Kui Lin, Yuan-Fu Liao, and Yih-Ru Wang. 2016. Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 73–81, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection (Chou et al., 2016)
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PDF:
https://aclanthology.org/W16-4910.pdf