{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:25:05Z","timestamp":1742945105833,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981830"},{"type":"electronic","value":"9789819981847"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8184-7_37","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T07:02:41Z","timestamp":1700895761000},"page":"483-494","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Two-Stage Graph Convolutional Networks for\u00a0Relation Extraction"],"prefix":"10.1007","author":[{"given":"Zhiqiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Chen, G., Tian, Y., Song, Y., Wan, X.: Relation extraction with type-aware map memories of word dependencies. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL\/IJCNLP 2021, Online Event, 1\u20136 August 2021. Findings of ACL, vol. ACL\/IJCNLP 2021, pp. 2501\u20132512. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.221","key":"37_CR1","DOI":"10.18653\/v1\/2021.findings-acl.221"},{"doi-asserted-by":"publisher","unstructured":"Denecke, K.: Sentiment Analysis in the Medical Domain. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-30187-2","key":"37_CR2","DOI":"10.1007\/978-3-031-30187-2"},{"doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","key":"37_CR3","DOI":"10.18653\/v1\/n19-1423"},{"doi-asserted-by":"crossref","unstructured":"Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 241\u2013251 (2019)","key":"37_CR4","DOI":"10.18653\/v1\/P19-1024"},{"unstructured":"Hendrickx, I., et al.: Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: Erk, K., Strapparava, C. (eds.) Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval@ACL 2010, Uppsala University, Uppsala, Sweden, 15\u201316 July 2010, pp. 33\u201338. The Association for Computer Linguistics (2010). https:\/\/aclanthology.org\/S10-1006\/","key":"37_CR5"},{"doi-asserted-by":"crossref","unstructured":"Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, 21\u201326 July 2004 - Poster and Demonstration. ACL (2004). https:\/\/aclanthology.org\/P04-3022\/","key":"37_CR6","DOI":"10.3115\/1219044.1219066"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)","key":"37_CR7"},{"key":"37_CR8","first-page":"101","volume":"5","author":"N Peng","year":"2017","unstructured":"Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.T.: Cross-sentence n-ary relation extraction with graph lstms. Trans. Assoc. Comput. Ling. 5, 101\u2013115 (2017)","journal-title":"Trans. Assoc. Comput. Ling."},{"key":"37_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116616","volume":"195","author":"M P\u00e9rez-P\u00e9rez","year":"2022","unstructured":"P\u00e9rez-P\u00e9rez, M., Ferreira, T., Igrejas, G., Fdez-Riverola, F.: A deep learning relation extraction approach to support a biomedical semi-automatic curation task: the case of the gluten bibliome. Expert Syst. Appl. 195, 116616 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.116616","journal-title":"Expert Syst. Appl."},{"key":"37_CR10","first-page":"20346","volume":"34","author":"S Sheng","year":"2021","unstructured":"Sheng, S., et al.: Human-adversarial visual question answering. Adv. Neural. Inf. Process. Syst. 34, 20346\u201320359 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"unstructured":"Soares, L.B., Fitzgerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: Distributional similarity for relation learning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2895\u20132905 (2019)","key":"37_CR11"},{"doi-asserted-by":"crossref","unstructured":"Song, L., Zhang, Y., Wang, Z., Gildea, D.: N-ary relation extraction using graph-state lstm. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2226\u20132235 (2018)","key":"37_CR12","DOI":"10.18653\/v1\/D18-1246"},{"doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, G., Song, Y., Wan, X.: Dependency-driven relation extraction with attentive graph convolutional networks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4458\u20134471 (2021)","key":"37_CR13","DOI":"10.18653\/v1\/2021.acl-long.344"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference 2021, pp. 878\u2013887 (2021)","key":"37_CR14","DOI":"10.1145\/3442381.3450133"},{"doi-asserted-by":"crossref","unstructured":"Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., Jin, Z.: Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1785\u20131794 (2015)","key":"37_CR15","DOI":"10.18653\/v1\/D15-1206"},{"doi-asserted-by":"crossref","unstructured":"Yan, Q., Zhang, Y., Liu, Q., Wu, S., Wang, L.: Relation-aware heterogeneous graph for user profiling. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3573\u20133577 (2021)","key":"37_CR16","DOI":"10.1145\/3459637.3482170"},{"doi-asserted-by":"crossref","unstructured":"Yu, B., Mengge, X., Zhang, Z., Liu, T., Yubin, W., Wang, B.: Learning to prune dependency trees with rethinking for neural relation extraction. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 3842\u20133852 (2020)","key":"37_CR17","DOI":"10.18653\/v1\/2020.coling-main.341"},{"unstructured":"Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083\u20131106 (2003). http:\/\/jmlr.org\/papers\/v3\/zelenko03a.html","key":"37_CR18"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2205\u20132215 (2018)","key":"37_CR19","DOI":"10.18653\/v1\/D18-1244"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8184-7_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T06:02:01Z","timestamp":1711346521000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8184-7_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981830","9789819981847"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8184-7_37","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}