{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T02:59:00Z","timestamp":1772593140959,"version":"3.50.1"},"reference-count":26,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2024,9,1]]},"DOI":"10.1587\/transinf.2023edp7166","type":"journal-article","created":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T22:19:18Z","timestamp":1725142758000},"page":"1264-1273","source":"Crossref","is-referenced-by-count":1,"title":["Chinese Spelling Correction Based on Knowledge Enhancement and Contrastive Learning"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Hao","family":"WANG","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]},{"given":"Yao","family":"MA","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]},{"given":"Jianyong","family":"DUAN","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]},{"given":"Li","family":"HE","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]},{"given":"Xin","family":"LI","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology"},{"name":"CNONIX National Standard Application and Promotion Lab"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] J. Yu and Z. Li, \u201cChinese spelling error detection and correction based on language model, pronunciation, and shape,\u201d Proc. Third CIPS-SIGHAN Joint Conference on Chinese Language Processing, pp.220-223, 2014. 10.3115\/v1\/w14-6835","DOI":"10.3115\/v1\/W14-6835"},{"key":"2","unstructured":"[2] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota. Association for Computational Linguistics, pp.4171-4186, 2019. 10.18653\/v1\/N19-1423"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] L. Zhang, M. Zhou, and C. Huang, \u201cAutomatic detecting\/correcting errors in Chinese text by an approximate word-matching algorithm,\u201d Proc. 38th Annual Meeting of the Association for Computational Linguistics, pp.248-254, 2000. 10.3115\/1075218.1075250","DOI":"10.3115\/1075218.1075250"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Y.-H. Tseng, L.-H. Lee, L.-P. Chang, and H.-H. Chen, \u201cIntroduction to sighan 2015 bake-off for Chinese spelling check,\u201d Proc. Eighth SIGHAN Workshop on Chinese Language Processing, pp.32-37, 2015. 10.18653\/v1\/w15-3106","DOI":"10.18653\/v1\/W15-3106"},{"key":"5","unstructured":"[5] J. Xiong, Q. Zhang, and S. Zhang, \u201cHANSpeller: a unified framework for Chinese spelling correction,\u201d International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 1, June 2015-Special Issue on Chinese as a Foreign Language, 2015. 10.18653\/v1\/W15-3107"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] W. Xie, P. Huang, X. Zhang, K. Hong, Q. Huang, B. Chen, and L. Huang, \u201cChinese spelling check system based on n-gram model,\u201d Proc. Eighth SIGHAN Workshop on Chinese Language Processing, pp.128-136, 2015. 10.18653\/v1\/w15-3120","DOI":"10.18653\/v1\/W15-3120"},{"key":"7","unstructured":"[7] J.F. Yeh, S.F. Li, and M.R. Wu, \u201cChinese word spelling correction based on n-gram ranked inverted index list,\u201d Proc. Seventh SIGHAN Workshop on Chinese Language Processing, pp.43-48, 2013."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] D. Wang, Y. Song, J. Li, J. Han, and H. Zhang, \u201cA hybrid approach to automatic corpus generation for Chinese spelling check,\u201d Proc. 2018 Conference on Empirical Methods in Natural Language Processing, pp.2517-2527, 2018. 10.18653\/v1\/d18-1273","DOI":"10.18653\/v1\/D18-1273"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Y. Hong, X. Yu, N. He, N. Liu, and J. Liu, \u201cFASPell: A fast, adaptable, simple, powerful Chinese spell checker based on DAE-decoder paradigm,\u201d Proc. 5th Workshop on Noisy User-generated Text (W-NUT 2019), pp.160-169, 2019. 10.18653\/v1\/D19-5522","DOI":"10.18653\/v1\/D19-5522"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Z. Guo, Y. Ni, K. Wang, W. Zhu, and G. Xie, \u201cGlobal attention decoder for Chinese spelling error correction,\u201d Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp.1419-1428, 2021. 10.18653\/v1\/2021.findings-acl.122","DOI":"10.18653\/v1\/2021.findings-acl.122"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. Zhang, H. Huang, J. Liu, and H. Li \u201cSpelling error correction with soft-masked BERT,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics., Seatle, Washington DC, USA, pp.882-890, 2020. 10.18653\/v1\/2020.acl-main.82","DOI":"10.18653\/v1\/2020.acl-main.82"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] X. Cheng, W. Xu, K. Chen, S. Jiang, F. Wang, T. Wang, W. Chu, and Y. Qi, \u201cSpellGCN: Incorporating phonological and visual similarities into language models for Chinese spelling check,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, pp.871-881, 2020. 10.18653\/v1\/2020.acl-main.81","DOI":"10.18653\/v1\/2020.acl-main.81"},{"key":"13","unstructured":"[13] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d arXiv preprint arXiv:1609.02907, 2016."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] L. Huang, J. Li, W. Jiang, Z. Zhang, M. Chen, S. Wang, and J. Xiao, \u201cPHMOSpell: Phonological and morphological knowledge guided Chinese spelling check,\u201d Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp.5958-5967, 2021. 10.18653\/v1\/2021.acl-long.464","DOI":"10.18653\/v1\/2021.acl-long.464"},{"key":"15","unstructured":"[15] T. Mikolov, I. Sutskever, and K. Chen, \u201cDistributed representations of words and phrases and their compositionality,\u201d Advances in Neural Information Processing Systems, p.26, 2013."},{"key":"16","unstructured":"[16] L. Logeswaran and H. Lee, \u201cAn efficient framework for learning sentence representations,\u201d arXiv preprint arXiv:1803.02893, 2018."},{"key":"17","unstructured":"[17] Z. Wu, S. Wang, and J. Gu, \u201cClear: Contrastive learning for sentence representation,\u201d arXiv preprint arXiv:2012.15466, 2020."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] R. Zhang, C. Pang, and C. Zhang, \u201cCorrecting Chinese spelling errors with phonetic pre-training,\u201d Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp.2250-2261, 2021.","DOI":"10.18653\/v1\/2021.findings-acl.198"},{"key":"19","unstructured":"[19] S.H. Wu, C.L. Liu, and L.H. Lee, \u201cChinese spelling check evaluation at SIGHAN bake-off 2013,\u201d Proc. Seventh SIGHAN Workshop on Chinese Language Processing, pp.35-42, 2013."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] L.-C. Yu, L.-H. Lee, Y.-H. Tseng, and H.-H. Chen, \u201cOverview of SIGHAN 2014 bake-off for Chinese spelling check,\u201d Proc. Third CIPS-SIGHAN Joint Conference on Chinese Language Processing, pp.126-132, 2014. 10.3115\/v1\/w14-6820","DOI":"10.3115\/v1\/W14-6820"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] B. Xu, Y. Xu, J. Liang, C. Xie, B. Liang, W. Cui, and Y. Xiao, \u201cCN-DBpedia: A never-ending Chinese knowledge extraction system[C],\u201d International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Cham: Springer International Publishing, vol.10351, pp.428-438, 2017. 10.1007\/978-3-319-60045-1_44","DOI":"10.1007\/978-3-319-60045-1_44"},{"key":"22","unstructured":"[22] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, \u201cRoberta: A robustly optimized bert pretraining approach,\u201d arXiv preprint arXiv:1907.11692, 2019."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Z. Sun, X. Li, X. Sun, Y. Meng, X. Ao, Q. He, F. Wu, and J. Li, \u201cChineseBERT: Chinese pretraining enhanced by glyph and pinyin information,\u201d Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1qeLong Papers), Bangkok, Thailand, pp.2065-2075, 2021. 10.18653\/v1\/2021.acl-long.161","DOI":"10.18653\/v1\/2021.acl-long.161"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] C. Zhu, Z. Ying, B. Zhang, and F. Mao, \u201cMDCSpell: A multi-task detector-corrector framework for Chinese spelling correction,\u201d Findings of the Association for Computational Linguistics, Dublin, Ireland, pp.1244-1253, 2022. 10.18653\/v1\/2022.findings-acl.98","DOI":"10.18653\/v1\/2022.findings-acl.98"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] H.-D. Xu, Z. Li, Q. Zhou, C. Li, Z. Wang, Y. Cao, H. Huang, and X.-L. Mao, \u201cRead, listen, and see: Leveraging multimodal information helps Chinese spell checking,\u201d Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp.716-728, 2021. 10.18653\/v1\/2021.findings-acl.64","DOI":"10.18653\/v1\/2021.findings-acl.64"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] B. Wang, W. Che, D. Wu, S. Wang, G. Hu, and T. Liu, \u201cDynamic connected networks for Chinese spelling check,\u201d Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp.2437-2446, 2021. 10.18653\/v1\/2021.findings-acl.216","DOI":"10.18653\/v1\/2021.findings-acl.216"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/9\/E107.D_2023EDP7166\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T16:25:15Z","timestamp":1732724715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/9\/E107.D_2023EDP7166\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":26,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7166","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,1]]},"article-number":"2023EDP7166"}}