Abstract
Distant supervision is an efficient approach for various tasks, such as relation extraction. Most of the recent literature on distantly supervised relation extraction generates labeled data by heuristically aligning knowledge bases with text corpora and then trains supervised relation classification models based on statistical learning. However, extracting long tail relations from the automatically labeled data is still a challenging problem even in big data. Inspired by explanation-based learning (EBL), this paper proposes an EBL-based approach to tackle this problem. The proposed approach can learn relation extraction rules effectively using unlabeled data. Experiments on the New York Times corpus demonstrate that our approach outperforms the baseline approach especially on long tail data.
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
Notes
- 1.
- 2.
- 3.
- 4.
The selected domains are not the same as in [15] due to different versions of Freebase.
References
Augenstein, I.: Seed selection for distantly supervised web-based relation extraction. In: Proceedings of SWAIE (2014)
Bing, L., Ling, M., Wang, R.C., Cohen, W.W.: Distant IE by bootstrapping using lists and document structure. In: AAAI (2016)
Craven, M., Kumlien, J.: Constructing biological knowledge bases by extracting information from text sources. ISMB 1999, 77–86 (1999)
Grave, E.: A convex relaxation for weakly supervised relation extraction. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
Han, X., Sun, L.: Global distant supervision for relation extraction. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 541–550. ACL (2011)
Intxaurrondo, A., Surdeanu, M., de Lacalle, O.L., Agirre, E.: Removing noisy mentions for distant supervision. Procesamiento del lenguaje natural 51, 41–48 (2013)
Koch, M., Gilmer, J., Soderland, S., Weld, D.S.: Type-aware distantly supervised relation extraction with linked arguments. In: Proceedings of EMNLP. ACL (2014)
Krause, S., Li, H., Uszkoreit, H., Xu, F.: Large-scale learning of relation-extraction rules with distant supervision from the web. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 263–278. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35176-1_17
Liu, Y., Liu, K., Xu, L., Zhao, J.: Exploring fine-grained entity type constraints for distantly supervised relation extraction. In: Proceedings of the 25th International Conference on Computational Linguistics, COLING 2014, Dublin, Ireland, pp. 2107–2166, August 2014. Technical Papers
Min, B., Grishman, R., Wan, L., Wang, C., Gondek, D.: Distant supervision for relation extraction with an incomplete knowledge base. In: HLT-NAACL, pp. 777–782 (2013)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of ACL-IJCNLP, vol. 2, pp. 1003–1011. ACL (2009)
Mitchell, T.M., Keller, R.M., Kedar-Cabelli, S.T.: Explanation-based generalization: a unifying view. Mach. Learn. 1(1), 47–80 (1986)
Pershina, M., Min, B., Xu, W., Grishman, R.: Infusion of labeled data into distant supervision for relation extraction. In: Proceedings of ACL (2014)
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). doi:10.1007/978-3-642-15939-8_10
Ritter, A., Zettlemoyer, L., Etzioni, O., et al.: Modeling missing data in distant supervision for information extraction. Trans. Assoc. Comput. Linguist. 1, 367–378 (2013)
Roller, R., Agirre, E., Soroa, A., Stevenson, M.: Improving distant supervision using inference learning. In: Proceedings of the ACL-IJCNLP, vol. 2, pp. 273–278. ACL, Beijing, July 2015. Short Papers
Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 455–465. ACL (2012)
Takamatsu, S., Sato, I., Nakagawa, H.: Reducing wrong labels in distant supervision for relation extraction. In: Proceedings of ACL, pp. 721–729. ACL (2012)
Wu, F., Weld, D.S.: Autonomously semantifying wikipedia. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 41–50. ACM (2007)
Xu, W., Hoffmann, R., Zhao, L., Grishman, R.: Filling knowledge base gaps for distant supervision of relation extraction. In: ACL (2), pp. 665–670 (2013)
Acknowledgement
This work is partially funded by the National Science Foundation of China under Grant 61170165, 61602260, 61502095.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Gui, Y., Liu, Q., Zhu, M., Gao, Z. (2016). Exploring Long Tail Data in Distantly Supervised Relation Extraction. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-50496-4_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50495-7
Online ISBN: 978-3-319-50496-4
eBook Packages: Computer ScienceComputer Science (R0)