@inproceedings{lee-etal-2016-overview,
title = "Overview of {NLP}-{TEA} 2016 Shared Task for {C}hinese Grammatical Error Diagnosis",
author = "Lee, Lung-Hao and
Rao, Gaoqi and
Yu, Liang-Chih and
Xun, Endong and
Zhang, Baolin and
Chang, Li-Ping",
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/W16-4906",
pages = "40--48",
abstract = "This paper presents the NLP-TEA 2016 shared task for Chinese grammatical error diagnosis which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 15 teams registered for this shared task, 9 teams developed the system and submitted a total of 36 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.",
}
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%0 Conference Proceedings
%T Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis
%A Lee, Lung-Hao
%A Rao, Gaoqi
%A Yu, Liang-Chih
%A Xun, Endong
%A Zhang, Baolin
%A Chang, Li-Ping
%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 lee-etal-2016-overview
%X This paper presents the NLP-TEA 2016 shared task for Chinese grammatical error diagnosis which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 15 teams registered for this shared task, 9 teams developed the system and submitted a total of 36 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.
%U https://aclanthology.org/W16-4906
%P 40-48
Markdown (Informal)
[Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis](https://aclanthology.org/W16-4906) (Lee et al., NLP-TEA 2016)
ACL