Chinese Grammatical Error Diagnosis System Based on Hybrid Model

Wu, Xiupeng; Huang, Peijie; Wang, Jundong; Qing-wen, Guo; Xu, Ye; Chen, Chuping

doi:10.18653/v1/w15-4418

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The wordlevel Levenshtein distance between source and target can be used as a translation model feature (Junczys-Dowmunt and Grundkiewicz, 2014) to enhance the model. Rule-based method and ngram statistical method are combined (Wu et al, 2015) to get a hybrid system for CGED shared task. Recently Napoles and Callison-Bursh (2017) propose a lightweight approach to GEC called Specialized Machine translation for Error Correction.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

Zhou

Bao

et al. 2018

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

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This paper introduces the DM NLP team's system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. This task aims at not only detecting four types of grammatical errors including redundant words (R), missing words (M), bad word selection (S) and disordered words (W), but also recommending corrections for errors of M and S types. We proposed a hybrid system including four models for this task with two stages: the detection stage and the correction stage. In the detection stage, we first used a BiLSTM-CRF model to tag potential errors by sequence labeling, along with some handcraft features. Then we designed three Grammatical Error Correction (GEC) models to generate corrections, which could help to tune the detection result. In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types. Our system reached the highest precision in the correction subtask, which was the most challenging part of this shared task, and got top 3 on F1 scores for position detection of errors.

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Section: Related Workmentioning

confidence: 99%

A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

Zhou

Bao

et al. 2018

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

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show abstract

“…For the NLPTEA 2015 shared task, The HITSZ system presented an ensemble learning based method to detect and identify grammatical errors (Xiang et al 2015). The SCAU system adopted a hybrid model by integrating rule-based and n-gram statistical methods for grammatical error diagnosis (Wu et al, 2015b). The CYUT team built an error diagnosis system based on the Conditional Random Fields (CRF) (Wu et al, 2015a).…”

Section: Shared Tasksmentioning

confidence: 99%

Developing learner corpus annotation for Chinese grammatical errors

Lee

Chang

Tseng

2016

2016 International Conference on Asian Language Processing (IALP)

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This study describes the construction of a TOCFL learner corpus and its usage for Chinese grammatical error diagnosis. We collected essays from the Test Of Chinese as a Foreign Language (TOCFL) and annotated grammatical errors using hierarchical tagging sets. Two kinds of error classifications were used simultaneously to tag grammatical errors. The first capital letter of each error tags denotes the coarse-grained surface differences, while the subsequent lowercase letters denote the fine-grained linguistic categories. A total of 33,835 grammatical errors in 2,837 essays and their corresponding corrections were manually annotated. We then used the Standard Generalized Markup Language to format learner texts and annotations along with learners' accompanying metadata. Parts of the TOCFL learner corpus have been provided for shared tasks on Chinese grammatical error diagnosis. We also investigated systems participating in the shared tasks to better understand current achievements and challenges. The datasets are publicly available to facilitate further research. To our best knowledge, this is the first annotated learner corpus of traditional Chinese, and the entire learner corpus will be publicly released.

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Chinese Grammatical Error Correction Using Statistical and Neural Models

Zhou

Liu

et al. 2018

Natural Language Processing and Chinese Computing

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Chinese Grammatical Error Diagnosis System Based on Hybrid Model

Cited by 6 publications

References 9 publications

A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

Developing learner corpus annotation for Chinese grammatical errors

Chinese Grammatical Error Correction Using Statistical and Neural Models

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