Error Detection and Correction Based on Chinese Phonemic Alphabet in Chinese Text

Huang, Chuen-Min; Wu, Ming-Hung; Chang, Ching-Che

doi:10.1007/978-3-540-73729-2_44

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…The statistics discussed in Section 4.3 show that a large portion of errors in Chinese texts are related to characters that have the same or similar pronunciations, and a previous work applied phonetic information for this error detection task based on related arguments [Huang et al 2008]. Using both visually and phonetically similar characters along with statistical methods, we significantly improve the performances of Huang et al's system and two other systems that were reported in the literature [Wu et al 2010].…”

Section: Applicationsmentioning

confidence: 99%

Visually and Phonologically Similar Characters in Incorrect Chinese Words

Liu

Lai

Tien

et al. 2011

ACM Transactions on Asian Language Information Processing

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Information about students' mistakes opens a window to an understanding of their learning processes, and helps us design effective course work to help students avoid replication of the same errors. Learning from mistakes is important not just in human learning activities; it is also a crucial ingredient in techniques for the developments of student models. In this article, we report findings of our study on 4,100 erroneous Chinese words. Seventy-six percent of these errors were related to the phonological similarity between the correct and the incorrect characters, 46% were due to visual similarity, and 29% involved both factors. We propose a computing algorithm that aims at replication of incorrect Chinese words. The algorithm extends the principles of decomposing Chinese characters with the Cangjie codes to judge the visual similarity between Chinese characters. The algorithm also employs empirical rules to determine the degree of similarity between Chinese phonemes. To show its effectiveness, we ran the algorithm to select and rank a list of about 100 candidate characters, from more than 5,100 characters, for the incorrectly written character in each of the 4,100 errors. We inspected whether the incorrect character was indeed included in the candidate list and analyzed whether the incorrect character was ranked at the top of the candidate list. Experimental results show that our algorithm captured 97% of incorrect characters for the 4,100 errors, when the average length of the candidate lists was 104. Further analyses showed that the incorrect characters ranked among the top 10 candidates in 89% of the phonologically similar errors and in 80% of the visually similar errors.

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Section: Applicationsmentioning

confidence: 99%

Visually and Phonologically Similar Characters in Incorrect Chinese Words

Liu

Lai

Tien

et al. 2011

ACM Transactions on Asian Language Information Processing

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“…20 from Section 3.3.1. Following previous studies (Chang, 1995;Huang et al, 2007;Wu et al, 2010;Dong et al, 2016), we adopt confusion sets 21 and a language model to handle the taks for Chinese spelling error correction. In particular, by collecting all incorrect variants for each correct character, we construct a confusion set for all involved correct characters, denoted as Ours.…”

Section: Chinese Spelling Error Correctionmentioning

confidence: 99%

“…In the line of research on spelling error detection and correction, most previous efforts focus on designing different models to improve the performance of CSC (Chang, 1995;Huang et al, 2007. Different from them, this work contributes to the generation of training datasets, which are important resources and can be used for improving many existing CSC models.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check

Wang¹,

Yan

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing (NLP) applications, but also facilitates reading and understanding of running texts in peoples' daily lives. However, to utilize datadriven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCRand ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach. * This work was conducted during Dingmin Wang's internship in Tencent AI Lab. Sentence Correction 我们应该认真对待这些己 (ji2) 经发生的事已 (yi3) 在我们班上，她 (ta1)是一个很聪明的男孩他 (ta1)

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“…Some Chinese spelling checkers have incorporated word segmentation technique. (Huang et al, 2007) used a word segmentation tool (CKIP) to generate correction candidates, and then to detect Chinese spelling errors. Some hybrid approach is applied to the Chinese spelling correction.…”

Section: Related Workmentioning

confidence: 99%

HANSpeller++: A Unified Framework for Chinese Spelling Correction

Zhang¹,

Xiong²,

Hou³

et al. 2015

Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

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Increased interest in China from foreigners has led to a corresponding interest in the study of Chinese. However, the learning of Chinese by non-native speakers will encounter many difficulties, Chinese spelling check techniques for Chinese as a Foreign Language(CFL) learners is highly desirable. This paper presents our work on the SIGHAN-2015 Chinese Spelling Check task. The task focuses on spelling checking on Chinese essays written by CFL learners. We propose a unified framework called HANSpeller++ based on our previous HANSpeller for Chinese spelling correction. The framework consists of candidate generating,candidates re-ranking and final global decision making. Experiments show good performance on the test data of the task.

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Error Detection and Correction Based on Chinese Phonemic Alphabet in Chinese Text

Cited by 13 publications

References 3 publications

Visually and Phonologically Similar Characters in Incorrect Chinese Words

Visually and Phonologically Similar Characters in Incorrect Chinese Words

A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check

HANSpeller++: A Unified Framework for Chinese Spelling Correction

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