{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:48:36Z","timestamp":1752230916057,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031059803"},{"type":"electronic","value":"9783031059810"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-05981-0_19","type":"book-chapter","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T12:02:50Z","timestamp":1652097770000},"page":"237-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improve Chinese Spelling Check by\u00a0Reevaluation"],"prefix":"10.1007","author":[{"given":"Shuai","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"19_CR1","unstructured":"Afli, H., Qiu, Z., Way, A., Sheridan, P.: Using SMT for OCR error correction of historical texts. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 962\u2013966 (2016)"},{"key":"19_CR2","unstructured":"Belinkov, Y., Bisk, Y.: Synthetic and natural noise both break neural machine translation. arXiv preprint arXiv:1711.02173 (2017)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Chang, T.H., Chen, H.C., Yang, C.H.: Introduction to a proofreading tool for Chinese spelling check task of SIGHAN-8. In: Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing, pp. 50\u201355 (2015)","DOI":"10.18653\/v1\/W15-3109"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Cheng, X., et al.: SpellGCN: incorporating phonological and visual similarities into language models for Chinese spelling check. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 871\u2013881. Association for Computational Linguistics, Online, July 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.81, https:\/\/aclanthology.org\/2020.acl-main.81","DOI":"10.18653\/v1\/2020.acl-main.81"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423, https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Dey, R., Salem, F.M.: Gate-variants of gated recurrent unit (GRU) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1597\u20131600. IEEE (2017)","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"19_CR7","unstructured":"Gao, J., et al.: A large scale ranker-based system for search query spelling correction (2010)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Guo, Z., Ni, Y., Wang, K., Zhu, W., Xie, G.: Global attention decoder for Chinese spelling error correction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1419\u20131428 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.122"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Hong, Y., Yu, X., He, N., Liu, N., Liu, J.: FASPell: a fast, adaptable, simple, powerful Chinese spell checker based on DAE-decoder paradigm. In: Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pp. 160\u2013169 (2019)","DOI":"10.18653\/v1\/D19-5522"},{"key":"19_CR10","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., Wilson, A.G.: Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 (2018)"},{"key":"19_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"19_CR12","unstructured":"Liu, C.L., Lai, M.H., Chuang, Y.H., Lee, C.Y.: Visually and phonologically similar characters in incorrect simplified Chinese words. In: Coling 2010: Posters, pp. 739\u2013747 (2010)"},{"key":"19_CR13","unstructured":"Liu, X., Cheng, K., Luo, Y., Duh, K., Matsumoto, Y.: A hybrid Chinese spelling correction using language model and statistical machine translation with reranking. In: Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing, pp. 54\u201358 (2013)"},{"key":"19_CR14","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in Adam (2018)"},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/978-3-540-30228-5_33","volume-title":"Advances in Natural Language Processing","author":"B Martins","year":"2004","unstructured":"Martins, B., Silva, M.J.: Spelling correction for search engine queries. In: Vicedo, J.L., Mart\u00ednez-Barco, P., Mu\u0144oz, R., Saiz Noeda, M. (eds.) EsTAL 2004. LNCS (LNAI), vol. 3230, pp. 372\u2013383. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30228-5_33"},{"key":"19_CR16","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"19_CR17","unstructured":"Sakaguchi, K., Mizumoto, T., Komachi, M., Matsumoto, Y.: Joint English spelling error correction and POS tagging for language learners writing. In: Proceedings of COLING 2012, pp. 2357\u20132374 (2012)"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Wang, B., Che, W., Wu, D., Wang, S., Hu, G., Liu, T.: Dynamic connected networks for chinese spelling check. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2437\u20132446 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.216"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Wang, D., Song, Y., Li, J., Han, J., Zhang, H.: A hybrid approach to automatic corpus generation for Chinese spelling check. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2517\u20132527 (2018)","DOI":"10.18653\/v1\/D18-1273"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Wang, D., Tay, Y., Zhong, L.: Confusionset-guided pointer networks for Chinese spelling check. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5780\u20135785 (2019)","DOI":"10.18653\/v1\/P19-1578"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Y.R., Liao, Y.F.: Word vector\/conditional random field-based Chinese spelling error detection for SIGHAN-2015 evaluation. In: Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing, pp. 46\u201349 (2015)","DOI":"10.18653\/v1\/W15-3108"},{"key":"19_CR22","unstructured":"Wu, S.H., Liu, C.L., Lee, L.H.: Chinese spelling check evaluation at SIGHAN bake-off 2013. In: Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing, pp. 35\u201342 (2013)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Xiong, J., Zhang, Q., Zhang, S., Hou, J., Cheng, X.: Hanspeller: a unified framework for Chinese spelling correction. In: International Journal of Computational Linguistics and Chinese Language Processing, vol. 20, No. 1, June 2015-Special Issue on Chinese as a Foreign Language (2015)","DOI":"10.18653\/v1\/W15-3107"},{"key":"19_CR24","doi-asserted-by":"publisher","unstructured":"Xu, H.D., et al.: Read, listen, and see: leveraging multimodal information helps Chinese spell checking. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 716\u2013728. Association for Computational Linguistics, Online, August 2021. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.64, https:\/\/aclanthology.org\/2021.findings-acl.64","DOI":"10.18653\/v1\/2021.findings-acl.64"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Correcting Chinese spelling errors with phonetic pre-training. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2250\u20132261 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.198"},{"key":"19_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, S., Huang, H., Liu, J., Li, H.: Spelling error correction with soft-masked BERT. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 882\u2013890. Association for Computational Linguistics, Online, July 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.82, https:\/\/aclanthology.org\/2020.acl-main.82","DOI":"10.18653\/v1\/2020.acl-main.82"},{"key":"19_CR27","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.ins.2021.03.044","volume":"562","author":"H Zou","year":"2021","unstructured":"Zou, H., et al.: On embedding sequence correlations in attributed network for semi-supervised node classification. Inf. Sci. 562, 385\u2013397 (2021)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-05981-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:40:21Z","timestamp":1710337221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-05981-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031059803","9783031059810"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-05981-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"558","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.45","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}