Abstract
Research works on relation extraction have put a lot of attention on finding features of surface text and syntactic patterns between entities. Much less work is done using semantically relevant features between entities because semantic information is difficult to identify without manual annotation. In this paper, we present a work for relation extraction using semantic information as we believe that semantic information is the most relevant and the least noisy for relation extraction. More specifically, we consider entity type matching as one of the additional feature because two entities of a relation must be confined to certain entity types. We further explore the use of trigger words which are semantically relevant to each relation type. Entity type matching controls the selective preference of arguments that participate in a relation. Trigger words add more positive evidences that are closely related to the target relations, which in turn help to reduce noisy data. To avoid manual annotation, we develop an automatic trigger word identification algorithm based on topic modeling techniques. Relation extraction is then carried out by incorporating these two types of semantic information in a graphical model along with other commonly used features. Performance evaluation shows that our relation extraction method is very effective, outperforming the state-of-the-art system on the CoNLL-2004 dataset by over 13 % in F-score and the baseline system without using these semantic information on Wikipedia data by over 12 %.
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Notes
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Other parameters used in LDA are not listed here.
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Other parameter values of LDA are α = 0.1, β = 0.1 with 100 iterations.
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To test this hypothesis, we manually examined 100 actual sentences for each relation type and found the margin of error to be within 15 %.
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References
Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 2670–2676 (2007)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Brin, S.: Extracting patterns and relations from the world wide web. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999)
Bunescu, R., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on HLT-EMNLP, pp. 724–731 (2005a)
Culotta, A., McCallum, A., Betz, J.: Integrating probabilistic extraction models and data mining to discover relations and patterns in text. In: Proceedings of the main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pp. 296–303 (2006)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Etzioni, O., Cafarella, M., Downey, D., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165(1), 91–134 (2005)
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Annual Meeting of the Association for Computational Linguistics (ACL), pp. 541–550 (2011)
Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL 2004 (2004)
Kate, R.J., Mooney, R.J.: Joint entity and relation extraction using card-pyramid parsing. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 203–212 (2010)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)
Liu, Y., Shi, Z., Sarkar, A.: Exploiting rich syntactic information for relation extraction from biomedical articles. In: The Conference of the North American Chapter of the Association for Computational Linguistics, pp. 97–100 (2007)
McCallum, A., Schultz, K., Singh, S.: Factorie: probabilistic programming via imperatively defined factor graphs. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 1249-1257 (2009)
McDonald, R., Pereira, F., Kulick, S., Winters, S., Jin, Y., White, P.: Simple algorithms for complex relation extraction with applications to biomedical IE. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 491–498 (2005)
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 (2009)
Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (2002)
Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part III. LNCS, vol. 6323, pp. 148–163. Springer, Heidelberg (2010)
Rosario, B., Hearst, M.A.: Classifying semantic relations in bioscience text. In: ACL 2004 (2004)
Roth, D., Yih, W.: Global inference for entity and relation identification via a linear programming formulation. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press (2007)
Wick, M., Rohanimanesh, K., Culotta, A., McCallum, A.: Samplerank: learning preferences from atomic gradients. In: Neural Information Processing Systems (NIPS), Workshop on Advances in Ranking (2009)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)
Zhao, S., Grishman, R.: Extracting relations with integrated information using kernel methods. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 419–426 (2005)
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Xu, J., Lu, Q., Li, M. (2016). Relation Extraction Using Semantic Information. In: Hasida, K., Purwarianti, A. (eds) Computational Linguistics. PACLING 2015. Communications in Computer and Information Science, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-10-0515-2_12
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