Abstract
The keep rising of web information ensures the development of entity focused information retrieval system. However, the problem of mining the relationships effectively between entities has not been well resolved. For the entity relationship extraction (RE) problem, this paper firstly establishes the basic pattern trees which can present the overall relation structures and then designs a similarity function according to which we can judge which pattern the sentence containing two entities belongs to. Knowing the matched pattern, we can discovery the relationship easily. By a large number of experiments on real data, the proposed methods are proved running accurately and efficiently.
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References
Banko, M., Etzioni, O.: The Tradeoffs Between Open and Traditional Relation Extraction. Proceedings of ACL, 28–36 (2008)
Lawrence, E., Rabiner, A.: Tutorial on Hidden Markov Models and Selected Application in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Berger, A.L., Pietra, S.A.D., Pietra, V.J.D.: A Maximum Entropy Approach to Natural Language Processing. Computational Linguistic 22(1), 39–71 (1996)
Li, W., Zhang, P., Wei, F., Hou, Y., Lu, Q.: A novel feature-based approach Chinese entity relation extraction. In: Human Language Technology Conference (2008)
Kambhatla, N.: Combining lexical, syntactic and semantic features whit Maximun Entropy models for extracting relations. In: ACL 2004, Barcelona, Spain, pp. 178–181 (2004)
Zhou, G., Su, J., Zhang, J., Zhang, M.: Exploring Various Knowledge in Relation Exctraction. Proceeding of ACL, 427–434 (2005)
Zelenko, D., Aone, C., Richardella, A.: Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 1083–1106 (2003)
Culotta, A., Sorensen, J.: Dependency Tree Kernels for Relation Extraction. Proceedings of ACL, 423–429 (2004)
Bunescu, R., Mooney, R.: A shortest Path Dependency Tree Kernel for Relation Extraction. In: Proceedings of HLT/EMNLP, pp. 724–731
Zhou, G., Zhang, M., Jiq, D.: Tree Kernel-based Relation Extraction with Context-Sensitive Structured Pare Tree Information. In: Proceedings of EMNLP, pp. 728–736 (2009)
Sekine, S.: On-demand information extraction. In: Proc.of COLING (2006)
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Chen, C., Liu, H., Wang, G., Ding, L., Yu, L. (2010). Entity Relationship Extraction Based on Potential Relationship Pattern. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds) Web Information Systems and Mining. WISM 2010. Lecture Notes in Computer Science, vol 6318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16515-3_46
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DOI: https://doi.org/10.1007/978-3-642-16515-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16514-6
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