{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:56:20Z","timestamp":1778324180895,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030821463","type":"print"},{"value":"9783030821470","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-82147-0_33","type":"book-chapter","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:26:36Z","timestamp":1628292396000},"page":"406-418","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Hierarchical Multi-label Text Classification: Self-adaption Semantic Awareness Network Integrating Text Topic and Label Level Information"],"prefix":"10.1007","author":[{"given":"Rui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Aly, R., Remus, S., Biemann, C.: Hierarchical multi-label classification of text with capsule networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pp. 323\u2013330 (2019)","DOI":"10.18653\/v1\/P19-2045"},{"key":"33_CR2","first-page":"307","volume":"2017","author":"S Baker","year":"2017","unstructured":"Baker, S., Korhonen, A.: Initializing neural networks for hierarchical multi-label text classification. BioNLP 2017, 307\u2013315 (2017)","journal-title":"BioNLP"},{"key":"33_CR3","first-page":"6359","volume":"33","author":"C Du","year":"2019","unstructured":"Du, C., Chen, Z., Feng, F., Zhu, L., Gan, T., Nie, L.: Explicit interaction model towards text classification. Proc. AAAI Conf. Artif. Intell. 33, 6359\u20136366 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Gopal, S., Yang, Y.: Recursive regularization for large-scale classification with hierarchical and graphical dependencies. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 257\u2013265 (2013)","DOI":"10.1145\/2487575.2487644"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Hao, P., Li, J., Yu, H., Liu, Y., Qiang, Y.: Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: The 2018 World Wide Web Conference, pp. 1063\u20131072 (2018)","DOI":"10.1145\/3178876.3186005"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Huang, W., Chen, E., Liu, Q., Chen, Y., Wang, S.: Hierarchical multi-label text classification: An attention-based recurrent network approach. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1051\u20131060 (2019)","DOI":"10.1145\/3357384.3357885"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Kowsari, K., Brown, D.E., Heidarysafa, M., Meimandi, K.J., Barnes, L.E.: Hdltex: hierarchical deep learning for text classification, pp. 364\u2013371 (2017)","DOI":"10.1109\/ICMLA.2017.0-134"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Kurata, G., Xiang, B., Zhou, B.: Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In: Proceedings of NAACL-HLT, pp. 521\u2013526 (2016)","DOI":"10.18653\/v1\/N16-1063"},{"key":"33_CR10","first-page":"2267","volume":"29","author":"S Lai","year":"2015","unstructured":"Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. Proc. AAAI Conf. Artif. Intell. 29, 2267\u20132273 (2015)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"33_CR11","first-page":"361","volume":"5","author":"DD Lewis","year":"2004","unstructured":"Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361\u2013397 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, H., Xu, S., Chang, B., Lei, S.: Multi-label text categorization with joint learning predictions-as-features method. In: Conference on Empirical Methods in Natural Language Processing, pp. 835\u2013839 (2015)","DOI":"10.18653\/v1\/D15-1099"},{"key":"33_CR13","unstructured":"Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 2873\u20132879 (2016)"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Mao, Y., Tian, J., Han, J., Ren, X.: Hierarchical text classification with reinforced label assignment. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pp. 445\u2013455 (2019)","DOI":"10.18653\/v1\/D19-1042"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Nam, J., Kim, J., Menc\u00eda, E., Gurevych, I., F\u00fcrnkranz, J.: Large-scale multi-label text classification - revisiting neural networks, pp. 437\u2013452 (2014)","DOI":"10.1007\/978-3-662-44851-9_28"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Prabhu, Y., Kag, A., Harsola, S., Agrawal, R., Varma, M.: Parabel: partitioned label trees for extreme classification with application to dynamic search advertising. In: the 2018 World Wide Web Conference, pp. 993\u20131002 (2018)","DOI":"10.1145\/3178876.3185998"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Shimura, K., Li, J., Fukumoto, F.: Hft-cnn: learning hierarchical category structure for multi-label short text categorization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 811\u2013816 (2018)","DOI":"10.18653\/v1\/D18-1093"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Wehrmann, J., Cerri, R., Barros, R.: Hierarchical multi-label classification networks. In: International Conference on Machine Learning, pp. 5075\u20135084. PMLR (2018)","DOI":"10.1145\/3019612.3019664"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Xiao, L., Huang, X., Chen, B., Jing, L.: Label-specific document representation for multi-label text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pp. 466\u2013475 (2019)","DOI":"10.18653\/v1\/D19-1044"},{"key":"33_CR20","unstructured":"Yang, P., Sun, X., Li, W., Ma, S., Wu, W., Wang, H.: Sgm: sequence generation model for multi-label classification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3915\u20133926 (2018)"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of NAACL-HLT, pp. 1480\u20131489 (2016)","DOI":"10.18653\/v1\/N16-1174"},{"key":"33_CR22","unstructured":"You, R., Zhang, Z., Wang, Z., Dai, S., Mamitsuka, H., Zhu, S.: Attentionxml: label tree-based attention-aware deep model for high-performance extreme multi-label text classification. Eprint Arxiv (2018)"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, J., Ma, C., Long, D., Xu, G., Liu, G.: Hierarchy-aware global model for hierarchical text classification. In: Association for Computational Linguistics, pp. 1106\u20131117 (2020)","DOI":"10.18653\/v1\/2020.acl-main.104"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82147-0_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:23:59Z","timestamp":1725582239000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82147-0_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030821463","9783030821470"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82147-0_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2021","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":"ksem2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/ksem21\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"492","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":"164","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":"33% - 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","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":"10","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)"}}]}}