{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T05:54:20Z","timestamp":1763790860736,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533459","type":"print"},{"value":"9789819533466","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3346-6_19","type":"book-chapter","created":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T05:50:09Z","timestamp":1763790609000},"page":"250-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DRA: A Dual Retrieval Architecture for\u00a0Domain Chinese Spelling Check"],"prefix":"10.1007","author":[{"given":"Haiming","family":"Wu","sequence":"first","affiliation":[]},{"given":"Zhinie","family":"Nie","sequence":"additional","affiliation":[]},{"given":"Songkun","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Dawei","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,23]]},"reference":[{"key":"19_CR1","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_CR2","unstructured":"Afli, H., Qui, Z., Way, A., Sheridan, P.: Using SMT for OCR error correction of historical texts. In: Proceedings of LREC 2016, Portoro\u017e, Slovenia, pp. 962\u2013966. European Language Resources Association (ELRA) (2016)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Dong, F., Zhang, Y.: Automatic features for essay scoring \u2013 an empirical study. In: Proceedings of EMNLP 2016, Austin, Texas, pp. 1072\u20131077. Association for Computational Linguistics (2016)","DOI":"10.18653\/v1\/D16-1115"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, S., Huang, H., Liu, J., Li, H.: Spelling error correction with soft-masked BERT. In: Proceedings of ACL 2020, Online, pp. 882\u2013890. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.82"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, X., Xu, W., Chen, K., et al.: SpellGCN: incorporating phonological and visual similarities into language models for Chinese spelling check. In: Proceedings of ACL 2020, Online, pp. 871\u2013881. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.81"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Wu, H., Zhang, S., Zhang, Y., Zhao, H.: Rethinking masked language modeling for Chinese spelling correction. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of ACL 2024, Toronto, Canada, pp. 10743\u201310756. Association for Computational Linguistics (2023)","DOI":"10.18653\/v1\/2023.acl-long.600"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Wu, H., Zhang, H., Xuan, R., Song, D.: Bi-DCSpell: a bi-directional detector-corrector interactive framework for Chinese spelling check. In: Findings of ACL: EMNLP 2024, Miami, Florida, USA, pp. 3974\u20133984. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.229"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Hong, Y., Yu, X., He, N., et al.: FASPell: a fast, adaptable, simple, powerful Chinese spell checker based on DAE-decoder paradigm. In: Proceedings of W-NUT 2019, Hong Kong, China, pp. 160\u2013169. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-5522"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Liu, L., Wu, H., Zhao, H.: Chinese spelling correction as rephrasing language model. In: Proceedings of AAAI 2024. AAAI Press (2024)","DOI":"10.1609\/aaai.v38i17.29829"},{"key":"19_CR10","unstructured":"Dong, M., Cheng, Z., Luo, C., He, T.: Retrieval-augmented generation for large language model based few-shot Chinese spell checking. In: Proceedings of COLING 2025, Abu Dhabi, UAE. Association for Computational Linguistics (2025)"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Mao, Y., He, P., Liu, X., et al.: Generation-augmented retrieval for open-domain question answering. In: Proceedings of ACL-IJCNLP 2021, Online, pp. 4089\u20134100. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-acl.29"},{"key":"19_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104662","volume":"156","author":"M Alkhalaf","year":"2024","unstructured":"Alkhalaf, M., Ping, Yu., Yin, M., Deng, C.: Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records. J. Biomed. Inform. 156, 104662 (2024)","journal-title":"J. Biomed. Inform."},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Fang, F., Bai, Y., Ni, S., et al.: Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training. In: Proceedings of ACL 2024, Bangkok, Thailand, pp. 10028\u201310039. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.acl-long.540"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhang, H., Pan, X., et al.: Chain-of-note: enhancing robustness in retrieval-augmented language models. In: Proceedings of EMNLP 2024, Miami, Florida, USA, pp. 14672\u201314685. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.813"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Hong, G., Kim, J., Kang, J., et al.: Why so gullible? Enhancing the robustness of retrieval-augmented models against counterfactual noise. In: Findings of ACL: NAACL 2024, Mexico City, Mexico, pp. 2474\u20132495. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.findings-naacl.159"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019, pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"19_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-030-84186-7_31","volume-title":"Chinese Computational Linguistics","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Lin, W., Shi, Y., Zhao, J.: A robustly optimized BERT pre-training approach with post-training. In: Li, S., et al. (eds.) CCL 2021. LNCS (LNAI), vol. 12869, pp. 471\u2013484. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-84186-7_31"},{"key":"19_CR18","unstructured":"Sun, Z., Li, X., Sun, X., et al.: ChineseBERT: Chinese pretraining enhanced by glyph and Pinyin information. In: Proceedings of ACL-IJCNLP 2021, Online, pp. 2065\u20132075. Association for Computational Linguistics (2021)"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Ji, T., Yan, H., Qiu, X.: SpellBERT: a lightweight pretrained model for Chinese spelling check. In: Proceedings of EMNLP 2021, Online and Punta Cana, Dominican Republic, pp. 3544\u20133551. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.287"},{"key":"19_CR20","unstructured":"Yin, X., Hu, X., Jiang, J., Wan, X.: Error-robust retrieval for Chinese spelling check. In: Proceedings of LREC-COLING 2024, Torino, Italia, pp. 6257\u20136267. ELRA and ICCL (2024)"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Lv, Q., Cao, Z., Geng, L., et al.: General and domain-adaptive Chinese spelling check with error-consistent pretraining. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 22(5) (2023)","DOI":"10.1145\/3564271"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Dong, M., Chen, Y., Zhang, M., et al.: Rich semantic knowledge enhanced large language models for few-shot Chinese spell checking. In: Findings of ACL 2024, Bangkok, Thailand, pp. 7372\u20137383. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.findings-acl.439"},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","volume":"8","author":"PH Le-Khac","year":"2020","unstructured":"Le-Khac, P.H., Healy, G., Smeaton, A.F.: Contrastive representation learning: a framework and review. IEEE Access 8, 193907\u2013193934 (2020)","journal-title":"IEEE Access"},{"key":"19_CR24","unstructured":"Dong, Q., Li, L., Dai, D., et al.: A survey on in-context learning. In: Proceedings of EMNLP 2024, Miami, Florida, USA, pp. 1107\u20131128. Association for Computational Linguistics (2024)"},{"key":"19_CR25","unstructured":"Li, C., Wang, J., Zhang, Y., et al.: Large language models understand and can be enhanced by emotional stimuli. arXiv preprint arXiv:2307.11760 (2023)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Xu, H.-D., Li, Z., Zhou, Q., et al.: Read, listen, and see: leveraging multimodal information helps Chinese spell checking. In: Findings of ACL-IJCNLP 2021, pp. 716\u2013728, Online. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-acl.64"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Q., Mao, Z., et al.: Improving Chinese spelling check by character pronunciation prediction: The effects of adaptivity and granularity. In: Proceedings of EMNLP 2022, Abu Dhabi, United Arab Emirates, pp. 4275\u20134286. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.287"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of EMNLP: System Demonstrations, pp. 38\u201345 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"19_CR29","unstructured":"Team GLM, Zeng, A., Xu, B., Wang, B., et al.: Chatglm: a family of large language models from GLM-130b to GLM-4 all tools. arXiv preprint arXiv:2406.12793 (2024)"},{"key":"19_CR30","unstructured":"Qwen Team. Qwen2.5: A party of foundation models (2024)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3346-6_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T05:50:16Z","timestamp":1763790616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3346-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"ISBN":["9789819533459","9789819533466"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3346-6_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]},"assertion":[{"value":"23 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2025\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}