{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:49:05Z","timestamp":1778215745364,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811961410","type":"print"},{"value":"9789811961427","type":"electronic"}],"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\/978-981-19-6142-7_9","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T16:05:36Z","timestamp":1666281936000},"page":"115-128","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Early Prediction and Label Smoothing Alignment Strategy for User Intent Classification of Medical Queries"],"prefix":"10.1007","author":[{"given":"Yuyu","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenjie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leung-Pun","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Choujun","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyong","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Cai, R., Zhu, B., Ji, L., Hao, T., Yan, J., Liu, W.: An CNN-LSTM attention approach to understanding user query intent from online health communities. In: 2017 IEEE International Conference on Data Mining Workshops, pp. 430\u2013437 (2017)","DOI":"10.1109\/ICDMW.2017.62"},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.knosys.2017.06.030","volume":"133","author":"T Hao","year":"2017","unstructured":"Hao, T., Xie, W., Wu, Q., et al.: Leveraging question target word features through semantic relation expansion for answer type classification. Knowl. Based Syst. 133, 43\u201352 (2017)","journal-title":"Knowl. Based Syst."},{"key":"9_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-319-68699-8_20","volume-title":"Information Retrieval","author":"W Xie","year":"2017","unstructured":"Xie, W., Gao, D., Hao, T.: A feature extraction and expansion-based approach for question target identification and classification. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds.) CCIR 2017. LNCS, vol. 10390, pp. 249\u2013260. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68699-8_20"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Shimura, K., Li, J., Fukumoto, F.: Text categorization by learning predominant sense of words as auxiliary task. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1109\u20131119 (2019)","DOI":"10.18653\/v1\/P19-1105"},{"key":"9_CR5","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, pp. 4171\u20134186 (2019)"},{"key":"9_CR6","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Guo, H., Liu, T., Liu, F., Li, Y., Hu, W.: Chinese text classification model based on bert and capsule network structure. In: 2021 7th IEEE International Conference on Big Data Security on Cloud, pp. 105\u2013110 (2021)","DOI":"10.1109\/BigDataSecurityHPSCIDS52275.2021.00029"},{"key":"9_CR8","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/978-981-15-7670-6_26","volume-title":"Neural Computing for Advanced Applications","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Liu, H., Wong, L.-P., Lee, L.-K., Zhang, H., Hao, T.: A hybrid neural network RBERT-C based on pre-trained RoBERTa and CNN for user intent classification. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2020. CCIS, vol. 1265, pp. 306\u2013319. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-7670-6_26"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Chen, J., Hu, Y., Liu, J., Xiao, Y., Jiang, H.: Deep short text classification with knowledge powered attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6252\u20136259 (2019)","DOI":"10.1609\/aaai.v33i01.33016252"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. arXiv preprint arXiv:1906.06906 (2019)","DOI":"10.18653\/v1\/P19-1048"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Zhao, S., Liu, T., Zhao, S., Wang, F.: A neural multi-task learning framework to jointly model medical named entity recognition and normalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 817\u2013824 (2019)","DOI":"10.1609\/aaai.v33i01.3301817"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Sun, K., Zhang, R., Mensah, S., Mao, Y., Liu, X.: Progressive multi-task learning with controlled information flow for joint entity and relation extraction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 13851\u201313859 (2021)","DOI":"10.1609\/aaai.v35i15.17632"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K, et al.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2267\u20132273. (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Shen, Y., Yao, J.: Recurrent neural network for text classification with hierarchical multiscale dense connections. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 5450\u20135456 (2019)","DOI":"10.24963\/ijcai.2019\/757"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 562\u2013570 (2017)","DOI":"10.18653\/v1\/P17-1052"},{"key":"9_CR16","unstructured":"Zhang, X., Wang, H.: A joint model of intent determination and slot filling for spoken language understanding. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 2993\u20132999 (2016)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Sun, X., Lu, W.: Understanding attention for text classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3418\u20133428 (2020)","DOI":"10.18653\/v1\/2020.acl-main.312"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2267\u20132273 (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, M., Deng, Z.: Densely connected CNN with multi-scale feature attention for text classification. In: Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 4468\u20134474 (2018)","DOI":"10.24963\/ijcai.2018\/621"},{"key":"9_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/978-3-030-84522-3_32","volume-title":"Intelligent Computing Theories and Application","author":"H Wu","year":"2021","unstructured":"Wu, H., He, Z., Zhang, W., Hu, Y., Wu, Y., Yue, Y.: Multi-class text classification model based on weighted word vector and BiLSTM-attention optimization. In: Huang, D.-S., Jo, K.-H., Li, J., Gribova, V., Bevilacqua, V. (eds.) ICIC 2021. LNCS, vol. 12836, pp. 393\u2013400. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-84522-3_32"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"He, C., Chen, S., Huang, S., Zhang, J., Song, X.: Using convolutional neural network with BERT for intent determination. In: 2019 International Conference on Asian Language Processing, pp. 65\u201370 (2019)","DOI":"10.1109\/IALP48816.2019.9037668"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Lin, Y., et al.: BertGCN: transductive text classification by combining GCN and BERT. arXiv preprint arXiv:2105.05727 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.126"},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","volume":"29","author":"Y Cui","year":"2021","unstructured":"Cui, Y., et al.: Pre-training with whole word masking for Chinese BERT. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 3504\u20133514 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Learning Intents behind Interactions with Knowledge Graph for Recommendation. In: Proceedings of the Web Conference, pp. 878\u2013887 (2021)","DOI":"10.1145\/3442381.3450133"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Zhang, Z., Zhang, W., Zhu, J.: BERT-KG: a short text classification model based on knowledge graph and deep semantics. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 721\u2013733 (2021)","DOI":"10.1007\/978-3-030-88480-2_58"},{"key":"9_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-1122-3","volume":"20","author":"N Chen","year":"2020","unstructured":"Chen, N., Su, X., Liu, T., Hao, Q., Wei, M.: A Benchmark dataset and case study for Chinese medical question intent classification. BMC Med. Inform. Decis. Mak. 20, 1\u20137 (2020)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"9_CR27","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.: When does label smoothing help. arXiv preprint arXiv:1906.02629 (2019)"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-6142-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T16:10:35Z","timestamp":1666282235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-6142-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811961410","9789811961427"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-6142-7_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","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":"8 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2022\/","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":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"205","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":"77","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":"38% - 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":"3.09","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":"3.68","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}