{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:36:20Z","timestamp":1742952980611,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030893620"},{"type":"electronic","value":"9783030893637"}],"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\/978-3-030-89363-7_23","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"299-310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Sentence-Level Relation Classification via Machine Reading Comprehension and Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Bo","family":"Xu","sequence":"first","affiliation":[]},{"given":"Zhengqi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiangsan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Song","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"issue":"1","key":"23_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"PT De Boer","year":"2005","unstructured":"De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19\u201367 (2005)","journal-title":"Ann. Oper. Res."},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column 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. 260\u2013269 (2015)","DOI":"10.3115\/v1\/P15-1026"},{"key":"23_CR3","unstructured":"Feng, J., Huang, M., Zhao, L., Yang, Y., Zhu, X.: Reinforcement learning for relation classification from noisy data. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, 2\u20137 February 2018, pp. 5779\u20135786 (2018)"},{"key":"23_CR4","unstructured":"Han, X., et al.: More data, more relations, more context and more openness: A review and outlook for relation extraction. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pp. 745\u2013758 (2020)"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Ji, G., Liu, K., He, S., Zhao, J.: Distant supervision for relation extraction with sentence-level attention and entity descriptions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, pp. 3060\u20133066 (2017)","DOI":"10.1609\/aaai.v31i1.10953"},{"key":"23_CR6","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"23_CR7","unstructured":"Kumar, S.: A survey of deep learning methods for relation extraction. arXiv preprint arXiv:1705.03645 (2017)"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., Li, J.: A unified MRC framework for named entity recognition. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5849\u20135859 (2020)","DOI":"10.18653\/v1\/2020.acl-main.519"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2124\u20132133 (2016)","DOI":"10.18653\/v1\/P16-1200"},{"key":"23_CR10","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003\u20131011 (2009)","DOI":"10.3115\/1690219.1690287"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Phi, V., Santoso, J., Tran, V., Shindo, H., Shimbo, M., Matsumoto, Y.: Distant supervision for relation extraction via piecewise attention and bag-level contextual inference. IEEE Access, 103570\u2013103582 (2019)","DOI":"10.1109\/ACCESS.2019.2932041"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Qin, P., Xu, W., Wang, W.Y.: Robust distant supervision relation extraction via deep reinforcement learning. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15\u201320 July 2018, vol. 1, Long Papers, pp. 2137\u20132147 (2018)","DOI":"10.18653\/v1\/P18-1199"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Jia, R., Liang, P.: Know what you don\u2019t know: Unanswerable questions for squad. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 784\u2013789 (2018)","DOI":"10.18653\/v1\/P18-2124"},{"key":"23_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/978-3-642-15939-8_10","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Riedel","year":"2010","unstructured":"Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balc\u00e1zar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 148\u2013163. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15939-8_10"},{"key":"23_CR16","unstructured":"Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, 12\u201314 July 2012, Jeju Island, Korea, pp. 455\u2013465 (2012)"},{"key":"23_CR17","unstructured":"Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139\u20131147. PMLR (2013)"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, pp. 1112\u20131119 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"23_CR19","unstructured":"Yoon, J., Arik, S., Pfister, T.: Data valuation using reinforcement learning. In: International Conference on Machine Learning, pp. 10842\u201310851. PMLR (2020)"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17\u201321 September 2015, pp. 1753\u20131762 (2015)","DOI":"10.18653\/v1\/D15-1203"},{"key":"23_CR21","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 23\u201329 August 2014, Dublin, Ireland, pp. 2335\u20132344 (2014)"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, T., et al.: Towards accurate and consistent evaluation: a dataset for distantly-supervised relation extraction. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 6436\u20136447 (2020)","DOI":"10.18653\/v1\/2020.coling-main.566"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89363-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T06:47:35Z","timestamp":1673678855000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89363-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893620","9783030893637"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89363-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","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":"382","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":"93","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":"28","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":"24% - 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":"5","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)"}}]}}