Improve the detection of improperly used Chinese characters in students' essays with error model

Chen, Yong Zhi; Wu, Shih-Hung; Yang, Ping Che; Ku, Tsun; Chen, Gwo Dong

doi:10.1504/ijceell.2011.039697

Cited by 23 publications

(16 citation statements)

References 6 publications

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“…The pattern matching approach is an old approach, which has been used in many previous works (Wu et al, 2010;Chen et al, 2011). The pattern contains frequent error terms, in which a character is replace by a similar one.…”

Section: Pattern-based Approachmentioning

confidence: 99%

CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task

Wang²,

Chen

et al. 2018

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

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This paper reports how we build a Chinese Grammatical Error Diagnosis system in the NLPTEA-2018 CGED shared task. In 2018, we sent three runs with three different approaches. The first one is a patternbased approach by frequent error pattern matching. The second one is a sequential labelling approach by conditional random fields (CRF). The third one is a rewriting approach by sequence to sequence (seq2seq) model. The three approaches have different properties that aim to optimize different performance metrics and the formal run results show the differences as we expected.

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Section: Pattern-based Approachmentioning

confidence: 99%

CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task

Wang²,

Chen

et al. 2018

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

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“…However, we find both will bring in lots of irrelevant characters though most errors have been discovered. Because Chen et al (2011) find the average amount of errors in a learners' corpus for a student essay is only 2, we do not want to mark too many error characters to cause false-alarm problem heavily.…”

Section: Spelling Error Detectionmentioning

confidence: 99%

Chinese Spelling Error Detection and Correction Based on Language Model, Pronunciation, and Shape

Yu¹,

Li²

2014

Proceedings of the Third CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Spelling check is an important preprocessing task when dealing with user generated texts such as tweets and product comments. Compared with some western languages such as English, Chinese spelling check is more complex because there is no word delimiter in Chinese written texts and misspelled characters can only be determined in word level. Our system works as follows. First, we use character-level n-gram language models to detect potential misspelled characters with low probabilities below some predefined threshold. Second, for each potential incorrect character, we generate a candidate set based on pronunciation and shape similarities. Third, we filter some candidate corrections if the candidate cannot form a legal word with its neighbors according to a word dictionary. Finally, we find the best candidate with highest language model probability. If the probability is higher than a predefined threshold, then we replace the original character; or we consider the original character as correct and take no action. Our preliminary experiments shows that our simple method can achieve relatively high precision but low recall.

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“…A CSC system will be evaluated in two levels: error detection and error correction. The task is organized based on some research works (Wu et al, 2010;Chen et al, 2011;Liu et al, 2011;Yu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

NTOU Chinese Spelling Check System in Sighan-8 Bake-off

Chu¹,

Lin

2015

Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

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This paper describes details of NTOU Chinese spelling check system in SIGHAN-8 Bakeoff. Besides the basic architecture of the previous system participating in last two CSC tasks, three new preference rules were proposed to deal with Simplified Chinese characters, variants, sentence-final particles, and DE-particles. A new sentence likelihood function was proposed based on frequencies of space-removed version of Google n-gram datasets. Two formal runs were submitted where the best one was created by the system using Google n-gram frequency information.

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Improve the detection of improperly used Chinese characters in students' essays with error model

Cited by 23 publications

References 6 publications

CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task

CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task

Chinese Spelling Error Detection and Correction Based on Language Model, Pronunciation, and Shape

NTOU Chinese Spelling Check System in Sighan-8 Bake-off

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