{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:36:00Z","timestamp":1743050160029,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031247545"},{"type":"electronic","value":"9783031247552"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-24755-2_8","type":"book-chapter","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T17:02:20Z","timestamp":1675357340000},"page":"90-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Learnable Graph Convolutional Neural Network Model for\u00a0Relation Extraction"],"prefix":"10.1007","author":[{"given":"Jinling","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yanping","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yongbin","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Ruizhang","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"8_CR1","unstructured":"Wang, L., Cardie, C.: Focused meeting summarization via unsupervised relation extraction. arXiv preprint arXiv:1606.07849 (2016)"},{"key":"8_CR2","unstructured":"Distiawan, B., Weikum, G., Qi, J., Zhang, R.: Neural relation extraction for knowledge base enrichment. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 229\u2013240 (2019)"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Xu, K., Reddy, S., Feng, Y., Huang, S., Zhao, D.: Question answering on freebase via relation extraction and textual evidence. arXiv preprint arXiv:1603.00957 (2016)","DOI":"10.18653\/v1\/P16-1220"},{"key":"8_CR4","first-page":"941","volume":"71","author":"K Xu","year":"2015","unstructured":"Xu, K., Feng, Y., Huang, S., Zhao, D.: Semantic relation classification via convolutional neural networks with simple negative sampling. Comput. Sci. 71, 941\u20139 (2015)","journal-title":"Comput. Sci."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Conference on Empirical Methods in Natural Language Processing (2015)","DOI":"10.18653\/v1\/D15-1203"},{"key":"8_CR6","unstructured":"Yan, X., Mou, L., Li, G., Chen, Y., Jin, Z.: Classifying relations via long short term memory networks along shortest dependency paths. In: The 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2015)"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. arXiv preprint arXiv:1601.00770 (2016)","DOI":"10.18653\/v1\/P16-1105"},{"key":"8_CR8","unstructured":"Veyseh, A.P.B., Dernoncourt, F., Dou, D., Nguyen, T.H.: Exploiting the syntax-model consistency for neural relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Fu, T.J., Ma, W.Y.: GraphRel: modeling text as relational graphs for joint entity and relation extraction. In: ACL (2019)","DOI":"10.18653\/v1\/P19-1136"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Vashishth, S., Joshi, R., Prayaga, S.S., Bhattacharyya, C., Talukdar, P.: RESIDE: improving distantly-supervised neural relation extraction using side information. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (2018)","DOI":"10.18653\/v1\/D18-1157"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. arXiv preprint arXiv:1809.10185 (2018)","DOI":"10.18653\/v1\/D18-1244"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. CoRR abs\/1906.07510 (2019)","DOI":"10.18653\/v1\/P19-1024"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Sun, K., Zhang, R., Mao, Y., Mensah, S., Liu, X.: Relation extraction with convolutional network over learnable syntax-transport graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8928\u20138935 (2020)","DOI":"10.1609\/aaai.v34i05.6423"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"13195","DOI":"10.1109\/ACCESS.2020.2966303","volume":"8","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Wang, K., Yang, W., Qing, Y., Huang, R., Chen, P.: A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195\u201313203 (2020)","journal-title":"IEEE Access"},{"key":"8_CR15","unstructured":"Rink, B., Harabagiu, S.: UTD: classifying semantic relations by combining lexical and semantic resources. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 256\u2013259 (2010)"},{"key":"8_CR16","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin City University and Association for Computational Linguistics, Dublin, Ireland, pp. 2335\u20132344 (2014). https:\/\/www.aclweb.org\/anthology\/C14-1220"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Alt, C., Gabryszak, A., Hennig, L.: Probing linguistic features of sentence-level representations in neural relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 1534\u20131545 (2020). https:\/\/www.aclweb.org\/anthology\/2020.acl-main.140","DOI":"10.18653\/v1\/2020.acl-main.140"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.3390\/sym13081458","volume":"13","author":"J Xu","year":"2021","unstructured":"Xu, J., Chen, Y., Qin, Y., Huang, R., Zheng, Q.: A feature combination-based graph convolutional neural network model for relation extraction. Symmetry 13, 1458 (2021)","journal-title":"Symmetry"},{"key":"8_CR19","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.P.: Composition-based multi-relational graph convolutional networks. CoRR abs\/1911.03082 (2019)"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Sun, C., Gong, Y., Wu, Y., Gong, M., Duan, N.: Joint type inference on entities and relations via graph convolutional networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1131"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Chen, D.: A frustratingly easy approach for joint entity and relation extraction. CoRR abs\/2010.12812 (2020)","DOI":"10.18653\/v1\/2021.naacl-main.5"},{"key":"8_CR22","doi-asserted-by":"publisher","first-page":"539","DOI":"10.3390\/sym13040539","volume":"13","author":"Y Qin","year":"2021","unstructured":"Qin, Y., et al.: Entity relation extraction based on entity indicators. Symmetry 13, 539 (2021)","journal-title":"Symmetry"},{"key":"8_CR23","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"8_CR24","unstructured":"Xu, J., Wen, J., Sun, X., Su, Q.: A discourse-level named entity recognition and relation extraction dataset for Chinese literature text. arXiv:1711.07010 (2019)"},{"key":"8_CR25","doi-asserted-by":"publisher","unstructured":"Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, ACLdemo 2004, p. 22-es. Association for Computational Linguistics, USA (2004). https:\/\/doi.org\/10.3115\/1219044.1219066","DOI":"10.3115\/1219044.1219066"},{"key":"8_CR26","unstructured":"Zhou, G., Su, J., Zhang, J., Zhang, M.: Exploring various knowledge in relation extraction. In: ACL 2005, 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 25\u201330 June 2005. University of Michigan, USA (2005)"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Gormley, M.R., Yu, M., Dredze, M.: Improved relation extraction with feature-rich compositional embedding models. CoRR abs\/1505.02419 (2015)","DOI":"10.18653\/v1\/D15-1205"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Christopoulou, F., Miwa, M., Ananiadou, S.: A walk-based model on entity graphs for relation extraction. In: The 56th Annual Meeting of the Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-2014"},{"key":"8_CR29","unstructured":"Veyseh, A.P.B., Nguyen, T.H., Dou, D.: Improving cross-domain performance for relation extraction via dependency prediction and information flow control. arXiv preprint arXiv:1907.03230 (2019)"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, W.: Two are better than one: joint entity and relation extraction with table-sequence encoders. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1706\u20131721. Association for Computational Linguistics (2020). https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.133","DOI":"10.18653\/v1\/2020.emnlp-main.133"},{"key":"8_CR31","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Hendrickx, I., et al.: SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, pp. 33\u201338. Association for Computational Linguistics, USA (2010)","DOI":"10.3115\/1621969.1621986"},{"key":"8_CR33","unstructured":"Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 151\u2013161. Association for Computational Linguistics, Edinburgh (2011). https:\/\/www.aclweb.org\/anthology\/D11-1014"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"dos Santos, C., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 626\u2013634. Association for Computational Linguistics, Beijing (2015). https:\/\/www.aclweb.org\/anthology\/P15-1061","DOI":"10.3115\/v1\/P15-1061"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wei, F., Li, S., Ji, H., Zhou, M., Wang, H.: A dependency-based neural network for relation classification. arXiv e-prints arXiv:1507.04646 (2015)","DOI":"10.3115\/v1\/P15-2047"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Cai, R., Zhang, X., Wang, H.: Bidirectional recurrent convolutional neural network for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 756\u2013765. Association for Computational Linguistics, Berlin (2016). https:\/\/www.aclweb.org\/anthology\/P16-1072","DOI":"10.18653\/v1\/P16-1072"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Wen, J., Sun, X., Ren, X., Su, Q.: Structure regularized neural network for entity relation classification for chinese literature text. arXiv:1803.05662 (2018)","DOI":"10.18653\/v1\/N18-2059"}],"container-title":["Lecture Notes in Computer Science","Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24755-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T17:05:13Z","timestamp":1675357513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24755-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031247545","9783031247552"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24755-2_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"3 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chongqing","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 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}