基于远程监督的关系抽取研究综述

白龙,靳小龙,席鹏弼,程学旗

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PDF(4194 KB)
中文信息学报 ›› 2019, Vol. 33 ›› Issue (10) : 10-17.
综述

基于远程监督的关系抽取研究综述

  • 白龙,靳小龙,席鹏弼,程学旗
作者信息 +

A Survey on Distant Supervision Based Relation Extraction

  • BAI Long, JIN Xiaolong, XI Pengbi, CHENG Xueqi
Author information +
History +

摘要

关系抽取作为信息抽取的一项关键技术,在知识库自动构建、问答系统等领域有着极为重要的意义,一直以来受到人们的关注。远程监督关系抽取技术通过外部知识库作为监督源,自动对语料库进行标注,能够大量节省人工标注成本,因而受到了研究者们的重视。该文针对远程监督关系抽取技术做了较为系统性的梳理,将已有方法分为基于概率图的、基于矩阵补全的和基于嵌入的三大类,并且对其当前面临的挑战进行了探讨,最后总结并展望了远程监督关系抽取技术未来的发展。

Abstract

As a key technique of information extraction, relation extraction is of great importance to many tasks such as automatic knowledge base construction and question answering systems. Distant supervision for relation extraction uses an external knowledge base as supervision signals to automatically label corpus, which can reduce the high cost of manual labelling. This paper presents a systematic survey to distantly supervised relation extraction. It classifies the existing methods into three types, including probabilistic graph-based, matrix completion-based and embedding-based ones. This paper also discusses the challenges and the future research directions of distantly supervised relation extraction.

关键词

远程监督 / 关系抽取 / 信息抽取

Key words

distant supervision / relation extraction / information extraction

引用本文

导出引用
白龙,靳小龙,席鹏弼,程学旗. 基于远程监督的关系抽取研究综述. 中文信息学报. 2019, 33(10): 10-17
BAI Long, JIN Xiaolong, XI Pengbi, CHENG Xueqi. A Survey on Distant Supervision Based Relation Extraction. Journal of Chinese Information Processing. 2019, 33(10): 10-17

参考文献

[1] Brin S. Extracting patterns and relations from the World Wide Web[C]//Proceedings of International Workshop on The World Wide Web and Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999:172-183.
[2] Sun A. Grishman R. Active learning for relation type extension with local and global data views[C]//Proceedings of the 21st ACM international conference on Information and knowledge management. Maui, Hawaii, USA: ACM, 2012:1105-1112.
[3] Chen J, Ji D, Tan C L, et al. Relation extraction using label propagation based semi-supervised learning[C]//Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, 2006:129-136.
[4] Mintz M, Bills S, Snow R, et al. Distant supervision for relation extraction without labeled data[C]//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. Suntec, Singapore: Association for Computational Linguistics, 2009:1003-1011.
[5] Banko M, Cafarella M J, Soderland S, et al. Open information extraction from the web[C]//Proceedings of the 20th international joint conference on Artifical intelligence. Hyderabad, India: Morgan Kaufmann Publishers Inc., 2007:2670-2676.
[6] Riedel S, Yao L, McCallum A. Modeling relations and their mentions without labeled text[C]//Proceedings of the Springer Berlin Heidelberg, Heidelberg,2010:148-163.
[7] Hoffmann R, Zhang C, Ling X, et al. Knowledge-based weak supervision for information extraction of overlapping relations[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, USA: Association for Computational Linguistics, 2011:541-550.
[8] Surdeanu M, Tibshirani J, Nallapati R, et al. Multi-instance multi-label learning for relation extraction[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Jeju Island, Korea: Association for Computational Linguistics, 2012:455-465.
[9] Bollacker K, Evans C, Paritosh P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD international conference on Management of data. Vancouver, Canada: ACM, 2008:1247-1250.
[10] Sandhaus E. The new york times annotated corpus[J].Linguistic Data Consortium, Philadelphia, 2008. 6(12): e26752.
[11] Ji H, Grishman R, Dang H T, et al. Overview of the TAC 2010 knowledge base population track[C]//Proceedings of the Third Text Analysis Conference (TAC 2010). 2010:3-13.
[12] Ji H, Grishman R, Dang H.Overview of the TAC2011 Knowledge Base Population Track[C]//Proceedings Text Analysis Conference. 2011.
[13] Zeng W, Lin Y, Liu Z, et al. Incorporating relation paths in neural relation extraction[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics, 2017:1768-1777.
[14] Vrande D. Wikidata: a free collaborative knowledgebase[J]. Commun. ACM, 2014. 57(10): 78-85.
[15] Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics Berlin, Germany: Association for Computational Linguistics, 2016:2124-2133.
[16] Takamatsu S, Sato I, Nakagawa H. Reducing wrong labels in distant supervision for relation extraction[C]//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju Island, Korea: Association for Computational Linguistics, 2012:721-729.
[17] Min B, Grishman R, Wan L, et al. Distant supervision for relation extraction with an incomplete knowledge base[C]//Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Atlanta, Georgia: Association for Computational Linguistics, 2013:777-782.
[18] Xu W, Hoffmann R, Zhao L, et al. Filling knowledge base gaps for distant supervision of relation extraction[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia, Bulgaria: Association for Computational Linguistics, 2013:665-670.
[19] Ritter A, Zettlemoyer L, Mausam, et al, Modeling missing data in distant supervision for information Extraction[J]. Transactions of the Association for Computational Linguistics, 2013, 1:367-378.
[20] Zeng D, Liu K, Chen Y, et al. Distant supervision for relation extraction via piecewise convolutional neural networks[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal: Association for Computational Linguistics, 2015:1753-1762.
[21] Fan M, Zhao D, Zhou Q, et al. Distant supervision for relation extraction with matrix completion[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, Maryland: Association for Computational Linguistics, 2014:839-849.
[22] Zhang Q, Wang H. Noise-Clustered distant supervision for relation extraction: A nonparametric bayesian perspective[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics, 2017:1808-1813.
[23] Weston J, Bordes A, Yakhnenko O, et al. Connecting language and knowledge bases with embedding models for relation extraction[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, Washington, USA: Association for Computational Linguistics, 2013:1366-1371.
[24] Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of Advances in neural information processing systems. 2013:2787-2795.
[25] Feng J, Huang M, Zhao L, et al. Reinforcement learning for relation classification from noisy data[C]//Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence. AAAI Press, 2018.
[26] Zeng X, He S, Liu K, et al. Large scaled relation extraction with reinforcement learning[C]//Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence. AAAI Press, 2018.
[27] Luo B, Feng Y, Wang Z, et al. Learning with noise: enhance distantly supervised relation extraction with Dynamic Transition Matrix[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada: Association for Computational Linguistics, 2017:430-439.
[28] Feng X, Guo J, Qin B, et al. Effective deep memory networks for distant supervised relation extraction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne, Australia: AAAI Press, 2017:4002-4008.
[29] Ye H, Chao W, Luo Z, et al. Jointly extracting relations with class ties via effective deep ranking[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.2017:1810-1820.
[30] Zheng S, Wang F, Bao H, et al. Joint extraction of entities and relations based on a novel tagging Scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017:1227-1236.
[31] Ren X, Wu Z, He W, et al. CoType: Joint extraction of typed entities and relations with knowledge bases[C]//Proceedings of the 26th International Conference on World Wide Web. Perth, Australia: International World Wide Web Conferences Steering Committee, 2017:1015-1024.
[32] Quirk C, Poon H. Distant supervision for relation extraction beyond the sentence boundary[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Valencia, Spain: Association for Computational Linguistics, 2017:1171-1182.
[33] Peng N, Poon H, Quirk C, et al, Cross-sentence n-ary relation extraction with graph LSTMs[J]. Transactions of the Association for Computational Linguistics, 2017,5:101-115.

基金

国家重点研发计划(2016YFB1000902);国家自然科学基金(61772501,61572473,61572469,91646120)
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