Chinese Spelling Check via Bidirectional LSTM-CRF

Duan, Jianyong; Wang, Bing; Tan, Zheng; Wei, Xiaopeng; Wang, Hao

doi:10.1109/itaic.2019.8785520

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…For CSC models based on deep learning, Duan et al [14] introduced a new neural network architecture integrating bidirectional LSTM model and CRF model, which took the character sequence of the sentence as input. The bidirectional LSTM layer first learns the character sequence information before sending the probability vectors to CRF layer, which then outputs best-predicted label sequence as the spelling detection result.…”

Section: Related Workmentioning

confidence: 99%

MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation

Jiang,

Shen,

Zhao

et al. 2024

Applied Sciences

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Chinese spelling errors are commonplace in our daily lives, which might be caused by input methods, optical character recognition, or speech recognition. Due to Chinese characters’ phonetic and visual similarities, the Chinese spelling check (CSC) is a very challenging task. However, the existing CSC solutions cannot achieve good spelling check performance since they often fail to fully extract the contextual information and Pinyin information. In this paper, we propose a novel CSC framework based on multi-label annotation (MLSL-Spell), consisting of two basic phases: spelling detection and correction. In the spelling detection phase, MLSL-Spell uses the fusion vectors of both character-based pre-trained context vectors and Pinyin vectors and adopts the sequence labeling method to explicitly label the type of misspelled characters. In the spelling correction phase, MLSL-Spell uses Masked Language Mode (MLM) model to generate candidate characters, then performs corresponding screenings according to the error types, and finally screens out the correct characters through the XGBoost classifier. Experiments show that the MLSL-Spell model outperforms the benchmark model. On SIGHAN 2013 dataset, the spelling detection F1 score of MLSL-Spell is 18.3% higher than that of the pointer network (PN) model, and the spelling correction F1 score is 10.9% higher. On SIGHAN 2015 dataset, the spelling detection F1 score of MLSL-Spell is 11% higher than that of Bert and 15.7% higher than that of the PN model. And the spelling correction F1 of MLSL-Spell score is 6.8% higher than that of PN model.

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Section: Related Workmentioning

confidence: 99%

MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation

Jiang,

Shen,

Zhao

et al. 2024

Applied Sciences

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“…Rule-based named entity recognition is the original entity recognition method [13], which is mainly applied to entities with contextual links or entities with special formats. Considering the cost and effectiveness of named entity recognition in the judicial field, Jiao et al [14] proposed a rule-based regular expression method for entity recognition to achieve the task of judicial language entity extraction.…”

Section: Rule-based Nermentioning

confidence: 99%

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

He¹,

Xu²,

Hu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must be desensitized before they are used as open resources. Therefore, to solve the above problems, data masking for Chinese electronic medical records with named entity recognition is proposed in this paper. Firstly, the text is vectorized to satisfy the required format of the model input. Secondly, since the input sentences may have a long or short length and the relationship between sentences in context is not negligible. To this end, a neural network model for named entity recognition based on bidirectional long shortterm memory (BiLSTM) with conditional random fields (CRF) is constructed. Finally, the data masking operation is performed based on the named entity recognition results, mainly using regular expression filtering encryption and principal component analysis (PCA) word vector compression and replacement. In addition, comparison experiments with the hidden markov model (HMM) model, LSTM-CRF model, and BiLSTM model are conducted in this paper. The experimental results show that the method used in this paper achieves 92.72% Accuracy, 92.30% Recall, and 92.51% F1_score, which has higher accuracy compared with other models.

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“…Word segmentation is another approach to detect errors using various methods such as CRF (Wang and Liao, 2014;Gu et al, 2014), graph-based algorithm (Jia et al, 2013;Xin et al, 2014;Zhao et al, 2017), or Hidden Markov Model (Xiong et al, 2014). Error detection has also been framed as a sequence tagging problem (Duan et al, 2019;Xie et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check

Nguyen¹,

Ngo²,

Chen³

2020

Preprint

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Spell check is a useful application which involves processing noisy human-generated text. Compared to other languages like English, it is more challenging to detect and correct spelling errors in Chinese because it has more (up to 100k) characters. For Chinese spell check, using confusion sets narrows the search space and makes finding corrections easier. However, most, if not all, confusion sets used to date are fixed and thus do not include new, evolving error patterns. We propose a scalable approach to adapt confusion sets by exploiting hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity issues related to seldomoccurring errors. Our approach establishes new SOTA results in spelling error correction on the 2014 and 2015 Chinese Spelling Correction Bake-off datasets.

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Chinese Spelling Check via Bidirectional LSTM-CRF

Cited by 10 publications

References 10 publications

MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation

MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check

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