{"type":"ScholarlyArticle","title":"Joint Extraction of Entities and Relations Based on a Novel Graph Scheme","authors":[{"name":"Shaolei Wang","slug":"shaolei-wang","given":"Shaolei","family":"Wang","affiliation":"Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Yue Zhang","slug":"yue-zhang","given":"Yue","family":"Zhang","affiliation":"Singapore University of Technology and Design"},{"name":"Wanxiang Che","slug":"wanxiang-che","given":"Wanxiang","family":"Che","affiliation":"Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":"Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"}],"abstract":"Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. Most existing neural joint methods extract entities and relations separately and achieve joint learning  through parameter sharing, leading to a drawback that information between output entities and relations cannot be fully exploited. In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations. Experiments on NewYork Times (NYT) corpora show that our approach outperforms the state-of-the-art methods.","doi":"10.24963/ijcai.2018/620","url":"https://ijcai.org/proceedings/2018/620","pdf":"https://ijcai.org/proceedings/2018/0620.pdf","markdown":"https://ijcai.org/proceedings/2018/620.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/620","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":620,"pages":{"first":4461,"last":4467},"keywords":["Natural Language Processing: Information Extraction","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Bunescu and Mooney, 2005] Razvan C Bunescu and Ray- mond J Mooney. A shortest path dependency kernel for relation extraction. In Proc. of EMNLP, 2005.","title":"A shortest path dependency kernel for relation extraction","authors":"C Razvan, Raymond J Bunescu, Mooney","year":"2005","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Choi and McCallum, 2013] Jinho D. Choi and Andrew Mc- Callum. Transition-based dependency parsing with selec- tional branching. In Proc. of ACL, pages 1052-1062, 2013.","title":"Transition-based dependency parsing with selectional branching","authors":"D Jinho, Andrew Choi, Mc-Callum","year":"2013","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dyer et al., 2015] Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. Transition- based dependency parsing with stack long short-term memory. In Proc. of ACL and IJCNLP, 2015.","title":"Transitionbased dependency parsing with stack long short-term memory","authors":"Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, Noah A Smith","year":"2015","venue":"Proc. of ACL and IJCNLP","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Florian et al., 2010] Radu Florian, John F Pitrelli, Salim Roukos, and Imed Zitouni. Improving mention detection robustness to noisy input. In Proc. of EMNLP, 2010.","title":"Improving mention detection robustness to noisy input","authors":"Radu Florian, John F Pitrelli, Salim Roukos, Imed Zitouni","year":"2010","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gormley et al., 2015] Matthew R Gormley, Mo Yu, and Mark Dredze. Improved relation extraction with feature- rich compositional embedding models. arXiv preprint arXiv:1505.02419, 2015.","title":"Improved relation extraction with featurerich compositional embedding models","authors":"Mo Matthew R Gormley, Mark Yu, Dredze","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hoffmann et al., 2011] Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. Knowledge-based weak supervision for information ex- traction of overlapping relations. In Proc. of ACL, pages 541-550, 2011.","title":"Knowledge-based weak supervision for information extraction of overlapping relations","authors":"Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, Daniel S Weld","year":"2011","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Katiyar and Cardie, 2017] Arzoo Katiyar and Claire Cardie. Going out on a limb: Joint extraction of entity mentions and relations without dependency trees. In Proc. of ACL, pages 917-928, July 2017.","title":"Going out on a limb: Joint extraction of entity mentions and relations without dependency trees","authors":"Cardie ; ] Arzoo Katiyar, Claire Katiyar, Cardie","year":"2017","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kiperwasser and Goldberg, 2016] Eliyahu Kiperwasser and Yoav Goldberg. Simple and accurate dependency pars- ing using bidirectional lstm feature representations. TACL, 4:313-327, 2016.","title":"Eliyahu Kiperwasser and Yoav Goldberg. Simple and accurate dependency parsing using bidirectional lstm feature representations","authors":"Goldberg Kiperwasser","year":"2016","venue":"TACL","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kuru et al., 2016] Onur Kuru, Ozan Arkan Can, and Deniz Yuret. Charner: Character-level named entity recognition. In Proc. of COLING, pages 911-921, December 2016.","title":"Charner: Character-level named entity recognition","authors":"Onur Kuru, Ozan Arkan Can, Deniz Yuret","year":"2016","venue":"Proc. of COLING","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lample et al., 2016] Guillaume Lample, Miguel Balles- teros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. Neural architectures for named entity recog- nition. In Proc. of NAACL, pages 260-270, June 2016.","title":"Neural architectures for named entity recognition","authors":"Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer","year":"2016","venue":"Proc. of NAACL","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li and Ji, 2014] Qi Li and Heng Ji. Incremental joint extrac- tion of entity mentions and relations. In ACL (1), 2014.","title":"Incremental joint extraction of entity mentions and relations","authors":"Ji ; ] Qi Li, Heng Li, Ji","year":"2014","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lin et al., 2016] Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. Neural relation extrac- tion with selective attention over instances. In Proc. of ACL, pages 2124-2133, August 2016.","title":"Neural relation extraction with selective attention over instances","authors":"Lin","year":"2016","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ling et al., 2015] Wang Ling, Chris Dyer, Alan Black, and Isabel Trancoso. Two/too simple adaptations of word2vec for syntax problems. In Proc. of NAACL, 2015.","title":"Two/too simple adaptations of word2vec for syntax problems","authors":"Ling","year":"2015","venue":"Proc. of NAACL","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu and Zhang, 2017] Jiangming Liu and Yue Zhang. In- order transition-based constituent parsing. Transactions of the Association for Computational Linguistics, 5, 2017.","title":"Inorder transition-based constituent parsing","authors":"Zhang Liu, Jiangming Liu, Yue Zhang","year":"2017","venue":"Transactions of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2015] Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou, and Houfeng WANG. A dependency-based neural network for relation classification. In Proc. of ACL, pages 285-290, 2015.","title":"A dependency-based neural network for relation classification","authors":"Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou, Wang Houfeng","year":"2015","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mintz et al., 2009] Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relation extrac- tion without labeled data. In Proc. of ACL, 2009.","title":"Distant supervision for relation extraction without labeled data","authors":"Mike Mintz, Steven Bills, Rion Snow, Dan Jurafsky","year":"2009","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Miwa and Bansal, 2016] Makoto Miwa and Mohit Bansal. End-to-end relation extraction using lstms on sequences and tree structures. In Proc. of ACL, 2016.","title":"End-to-end relation extraction using lstms on sequences and tree structures","authors":"Bansal Miwa, Makoto Miwa, Mohit Bansal","year":"2016","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Miwa and Sasaki, 2014] Makoto Miwa and Yutaka Sasaki. Modeling joint entity and relation extraction with table representation. In EMNLP, pages 1858-1869, 2014.","title":"Modeling joint entity and relation extraction with table representation","authors":"Sasaki Miwa, Makoto Miwa, Yutaka Sasaki","year":"2014","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Nadeau and Sekine, 2007] David Nadeau and Satoshi Sekine. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1):3-26, 2007.","title":"A survey of named entity recognition and classification","authors":"Sekine Nadeau, David Nadeau, Satoshi Sekine","year":"2007","venue":"Lingvisticae Investigationes","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Nivre, 2008] Joakim Nivre. Algorithms for deterministic in- cremental dependency parsing. Computational Linguis- tics, 34(4):513-553, 2008.","title":"Algorithms for deterministic incremental dependency parsing","authors":"Joakim Nivre, Nivre","year":"2008","venue":"Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ren et al., 2017] Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R Voss, Heng Ji, Tarek F Abdelzaher, and Ji- awei Han. Cotype: Joint extraction of typed entities and relations with knowledge bases. In Proc. of the 26th Inter- national Conference on World Wide Web, 2017.","title":"Cotype: Joint extraction of typed entities and relations with knowledge bases","authors":"Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R Voss, Heng Ji, Jiawei Tarek F Abdelzaher, Han","year":"2017","venue":"Proc. of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proc. of the 24th International Conference on World Wide Web, 2015.","title":"Line: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proc. of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2018] Yuxuan Wang, Wanxiang Che, Jiang Guo, and Ting Liu. A neural transition-based approach for semantic dependency graph parsing. 2018.","title":"A neural transition-based approach for semantic dependency graph parsing","authors":"Yuxuan Wang, Wanxiang Che, Jiang Guo, Ting Liu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang and Clark, 2011] Yue Zhang and Stephen Clark. Syntactic processing using the generalized perceptron and beam search. Computational linguistics, 37(1):105-151, 2011.","title":"Syntactic processing using the generalized perceptron and beam search","authors":"Yue Zhang, Stephen Clark","year":"2011","venue":"Computational linguistics","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zheng et al., 2017] Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, Peng Zhou, and Bo Xu. Joint extraction of entities and relations based on a novel tagging scheme. In Proc. of ACL, pages 1227-1236, July 2017.","title":"Joint extraction of entities and relations based on a novel tagging scheme","authors":"Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, Peng Zhou, Bo Xu","year":"2017","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhou et al., 2005] GuoDong Zhou, Jian Su, Jie Zhang, and Min Zhang. Exploring various knowledge in relation ex- traction. In Proc. of ACL, pages 427-434, 2005.","title":"Exploring various knowledge in relation extraction","authors":"Guodong Zhou, Jian Su, Jie Zhang, Min Zhang","year":"2005","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhou et al., 2015] Hao Zhou, Yue Zhang, Shujian Huang, and Jiajun Chen. A neural probabilistic structured- prediction model for transition-based dependency parsing. In Proc. of ACL, pages 1213-1222, 2015.","title":"A neural probabilistic structuredprediction model for transition-based dependency parsing","authors":"Hao Zhou, Yue Zhang, Shujian Huang, Jiajun Chen","year":"2015","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}