Abstract
A classifier-based pattern selection approach for relation instance extraction is proposed in this paper. The classifier-based pattern selection approach proposes to employ a binary classifier that filters patterns that extracts incorrect entities for a given relation, from pattern set obtained using global estimates such as high frequency. The proposed approach is evaluated using two large independent datasets. The results presented in this paper shows that the classifier-based approach provides a significant improvement in the task of relation extraction against standard methods of relation extraction, employing pattern sets based on high frequency. The higher performance is achieved through filtering out patterns that extract incorrect entities, which in turn improves the precision of applied patterns, resulting in significant improvement in the task of relation extraction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the COLING, pp. 41–47. Association for Computational Linguistics (2002)
Wu, F., Weld, D.S.: Open information extraction using Wikipedia. In: Proceedings of COLING, pp. 118–127. Association for Computational Linguistics (2010)
Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam, M.: Open information extraction: the second generation. Proc. IJCAI. 11, 3–10 (2011)
Gamallo, P., Garcia, M., Fernández-Lanza, S.: Dependency-based open information extraction. In: Proceedings of the Joint Workshop on Unsupervised and Semi-supervised Learning in NLP, pp. 10–18. Association for Computational Linguistics (2012)
Kim, J.T., Moldovan, D.: Acquisition of linguistic patterns for knowledge-based information extraction. IEEE Trans. Knowl. Data Eng. 7, 713–724 (1995)
Moschitti, A., Morarescu, P., Harabagiu, S.M.: Open domain information extraction via automatic semantic labeling. In: Proceedings of FLAIRS, pp. 397–401 (2003)
Shen, D., Lapata, M.: Using semantic roles to improve question answering. In: Proceedings of EMNLP-CoNLL, pp. 12–21 (2007)
Søgaard, A., Plank, B., Alonso, H.M.: Using frame semantics for knowledge extraction from twitter. In: Proceedings of AAAI (2015)
Riloff, E.: Automatically generating extraction patterns from untagged text. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1044–1049 (1996)
Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 85–94. ACM (2000)
Yangarber, R., Lin, W., Grishman, R.: Unsupervised learning of generalized names. In: Proceedings of COLING, pp. 1–7. Association for Computational Linguistics (2002)
Lin, W., Yangarber, R., Grishman, R.: Bootstrapped learning of semantic classes from positive and negative examples. In: Proceedings of ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data, vol. 1, p. 21 (2003)
Gupta, S., Manning, C.D.: Improved pattern learning for bootstrapped entity extraction. In: CoNLL, pp. 98–108 (2014)
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: Volume 2, vol. 2, pp. 1003–1011. Association for Computational Linguistics (2009)
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. LNCS (LNAI), vol. 6323, pp. 148–163. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15939-8_10
Brin, S.: Extracting patterns and relations from the world wide web. In: Atzeni, P., Mendelzon, A., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999). https://doi.org/10.1007/10704656_11
Thelen, M., Riloff, E.: A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In: Proceedings of EMNLP, pp. 214–221. Association for Computational Linguistics (2002)
Patwardhan, S., Riloff, E.: Learning domain-specific information extraction patterns from the web. In: Proceedings of the Workshop on Information Extraction beyond the Document, pp. 66–73. Association for Computational Linguistics (2006)
Patwardhan, S., Riloff, E.: Effective information extraction with semantic affinity patterns and relevant regions. Proc. EMNLP-CoNLL 7, 717–727 (2007)
Alfonseca, E., Filippova, K., Delort, J.Y., Garrido, G.: Pattern learning for relation extraction with a hierarchical topic model. In: Proceedings of the 50th Annual Meeting of the ACL, pp. 54–59. Association for Computational Linguistics (2012)
GuoDong, Z., Jian, S., Jie, Z., Min, Z.: Exploring various knowledge in relation extraction. In: Proceedings of COLING, pp. 427–434. Association for Computational Linguistics (2005)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)
De Marneffe, M.C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. Proc. LREC 6, 449–454 (2006)
Fillmore, C.: Frame semantics. In: Linguistics in the Morning Calm, pp. 111–137 (1982)
Das, D., Chen, D., Martins, A.F., Schneider, N., Smith, N.A.: Frame-semantic parsing. Comput. Linguist. 40, 9–56 (2014)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Ives, Z.: DBpedia: a nucleus for a web of open data. In: 6th International Semantic Web Conference, Busan, Korea (2007)
Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Mandya, A., Bollegala, D., Coenen, F., Atkinson, K. (2018). Classifier-Based Pattern Selection Approach for Relation Instance Extraction. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-77113-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77112-0
Online ISBN: 978-3-319-77113-7
eBook Packages: Computer ScienceComputer Science (R0)