acl acl2011 acl2011-126 acl2011-126-reference knowledge-graph by maker-knowledge-mining
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Author: Yee Seng Chan ; Dan Roth
Abstract: In this paper, we observe that there exists a second dimension to the relation extraction (RE) problem that is orthogonal to the relation type dimension. We show that most of these second dimensional structures are relatively constrained and not difficult to identify. We propose a novel algorithmic approach to RE that starts by first identifying these structures and then, within these, identifying the semantic type of the relation. In the real RE problem where relation arguments need to be identified, exploiting these structures also allows reducing pipelined propagated errors. We show that this RE framework provides significant improvement in RE performance.
Peter F. Brown, Vincent J. Della Pietra, Peter V. deSouza, Jenifer C. Lai, and Robert L. Mercer. 1992. Classbased n-gram models of natural language. Computational Linguistics, 18(4):467–479. Yee Seng Chan and Dan Roth. 2010. Exploiting background knowledge for relation extraction. In Proceedings ofthe International Conference on Computational Linguistics (COLING), pages 152–160. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, XiangRui Wang, and Chih-Jen Lin. 2008. Liblinear: A library for large linear classification. Journal of Machine Learning Research, 9: 1871–1874. Katrin Fundel, Robert K ¨uffner, and Ralf Zimmer. 2007. Relex Relation extraction using dependency parse trees. Bioinformatics, 23(3):365–371. Mark A. Greenwood and Mark Stevenson. 2006. Improving semi-supervised acquisition of relation extraction patterns. In Proceedings of the COLING-ACL Workshop on Information Extraction Beyond The Document, pages 29–35. Jing Jiang and ChengXiang Zhai. 2007. A systematic exploration of the feature space for relation extraction. In Proceedings of Human Language Technologies North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pages 113–120. Jing Jiang. 2009. Multi-task transfer learning for weakly-supervised relation extraction. In Proceedings of the Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), pages 1012–1020. Nanda Kambhatla. 2004. Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 178–181 . – Dan Klein and Christoper D. Manning. 2003. Fast exact inference with a factored model for natural language parsing. In The Conference on Advances in Neural Information Processing Systems (NIPS), pages 3–10. Longhua Qian, Guodong Zhou, Qiaomin Zhu, and Peide Qian. 2008. Relation extraction using convolution tree kernel expanded with entity features. In Pacific Asia Conference on Language, Information and Computation, pages 415–421. Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the Annual Conference on Computational Natural Language Learning (CoNLL), pages 147–155. Dan Roth and Wen Tau Yih. 2007. Global inference for entity and relation identification via a linear programming formulation. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. MIT Press. Guodong Zhou, Jian Su, Jie Zhang, and Min Zhang. 2005. Exploring various knowledge in relation extraction. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 427–434. Jun Zhu, Zaiqing Nie, Xiaojiang Liu, Bo Zhang, and JiRong Wen. 2009. Statsnowball: a statistical approach to extracting entity relationships. In The International World Wide Web Conference, pages 101–1 10. 560