@inproceedings{chen-etal-2016-cyut,
title = "{CYUT}-{III} System at {C}hinese Grammatical Error Diagnosis Task",
author = "Chen, Po-Lin and
Wu, Shih-Hung and
Chen, Liang-Pu and
Yang, Ping-Che",
editor = "Chen, Hsin-Hsi and
Tseng, Yuen-Hsien and
Ng, Vincent and
Lu, Xiaofei",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4909",
pages = "63--72",
abstract = "This paper describe the CYUT-III system on grammar error detection in the 2016 NLP-TEA Chinese Grammar Error Detection shared task CGED. In this task a system has to detect four types of errors, in-cluding redundant word error, missing word error, word selection error and word ordering error. Based on the conditional random fields (CRF) model, our system is a linear tagger that can detect the errors in learners{'} essays. Since the system performance depends on the features heavily, in this paper, we are going to report how to integrate the collocation feature into the CRF model. Our system presents the best detection accuracy and Identification accuracy on the TOCFL dataset, which is in traditional Chi-nese. The same system also works well on the simplified Chinese HSK dataset.",
}
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<abstract>This paper describe the CYUT-III system on grammar error detection in the 2016 NLP-TEA Chinese Grammar Error Detection shared task CGED. In this task a system has to detect four types of errors, in-cluding redundant word error, missing word error, word selection error and word ordering error. Based on the conditional random fields (CRF) model, our system is a linear tagger that can detect the errors in learners’ essays. Since the system performance depends on the features heavily, in this paper, we are going to report how to integrate the collocation feature into the CRF model. Our system presents the best detection accuracy and Identification accuracy on the TOCFL dataset, which is in traditional Chi-nese. The same system also works well on the simplified Chinese HSK dataset.</abstract>
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%0 Conference Proceedings
%T CYUT-III System at Chinese Grammatical Error Diagnosis Task
%A Chen, Po-Lin
%A Wu, Shih-Hung
%A Chen, Liang-Pu
%A Yang, Ping-Che
%Y Chen, Hsin-Hsi
%Y Tseng, Yuen-Hsien
%Y Ng, Vincent
%Y Lu, Xiaofei
%S Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chen-etal-2016-cyut
%X This paper describe the CYUT-III system on grammar error detection in the 2016 NLP-TEA Chinese Grammar Error Detection shared task CGED. In this task a system has to detect four types of errors, in-cluding redundant word error, missing word error, word selection error and word ordering error. Based on the conditional random fields (CRF) model, our system is a linear tagger that can detect the errors in learners’ essays. Since the system performance depends on the features heavily, in this paper, we are going to report how to integrate the collocation feature into the CRF model. Our system presents the best detection accuracy and Identification accuracy on the TOCFL dataset, which is in traditional Chi-nese. The same system also works well on the simplified Chinese HSK dataset.
%U https://aclanthology.org/W16-4909
%P 63-72
Markdown (Informal)
[CYUT-III System at Chinese Grammatical Error Diagnosis Task](https://aclanthology.org/W16-4909) (Chen et al., NLP-TEA 2016)
ACL
- Po-Lin Chen, Shih-Hung Wu, Liang-Pu Chen, and Ping-Che Yang. 2016. CYUT-III System at Chinese Grammatical Error Diagnosis Task. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 63–72, Osaka, Japan. The COLING 2016 Organizing Committee.