Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis

Lee, Lung-Hao; Yu, Liang-Chih; Chang, Liping

doi:10.18653/v1/w15-4401

Cited by 29 publications

(25 citation statements)

References 2 publications

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“…Many researchers have chosen CRF based models to solve CGED2016 and CGED2017 task. From previous research, we know that the CRF model with carefully designed feature templates could maintain the performance with neural networks at the same level (Lung-Hao Lee et al, 2016), especially when the training data is not big enough. Another is LSTM-CRF model with conventional sparse CRF features.…”

Section: Methodsmentioning

confidence: 99%

Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model

Zhou

Yinan²,

Yong³

2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages. However, few grammar checking applications have been developed to support the learners. The goal of this paper is to develop a tool to automatically diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W) in Chinese sentences written by those foreign learners. In this paper, a conventional linear CRF model with specific feature engineering and a LSTM-CRF model are used to solve the CGED (Chinese Grammatical Error Diagnosis) task. We make some improvement on both models and the submitted results have better performance on false positive rate and accuracy than the average of all runs from CGED2018 for all three evaluation levels.

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Section: Methodsmentioning

confidence: 99%

Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model

Zhou

Yinan²,

Yong³

2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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“…However, due to the lack of corpora and the limitations of technology, the research progress is limited greatly. The CGED shared tasks (Yu et al, 2014;Lee et al, 2015Lee et al, , 2016RAO et al, 2017) provided researchers with a good platform to present their work. In CGED2016 shared task, a CRF-based model achieved good precision (Liu et al, 2016) and a model based on CRF+LSTM get good results (Zheng et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model

Liu¹,

Zan²,

Zhong³

et al. 2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.

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“…In response to the limited availability of CFL learner data for machine learning and linguistic analysis, the ICCE-2014 workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA) organized a shared task on diagnosing grammatical errors for CFL (Yu et al, 2014). A second version of this shared task in NLP-TEA was collocated with the ACL-IJCNLP-2015 (Lee et al, 2015), COLING-2016 . Its name was fixed from then on: Chinese Grammatical Error Diagnosis (CGED).…”

Section: Introductionmentioning

confidence: 99%

Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis

Rao

Gong²,

Zhang³

et al. 2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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This paper presents the NLPTEA 2018 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 20 teams registered for this shared task, 13 teams developed the system and submitted a total of 32 runs. Progress in system performances was obviously, reaching F1 of 36.12% in position level and 25.27% in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.

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Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis

Cited by 29 publications

References 2 publications

Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model

Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model

Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model

Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis

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