Family relation extraction from Wikipedia by self-supervised learning

doi:10.11772/j.issn.1001-9081.2015.04.1013

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (4): 1013-1016.DOI: 10.11772/j.issn.1001-9081.2015.04.1013

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Family relation extraction from Wikipedia by self-supervised learning

ZHU Suyang^1,2, HUI Haotian^1,2, QIAN Longhua^1,2, ZHANG Min^1,2

1. Natural Language Processing Laboratory, Soochow University, Suzhou Jiangsu 215006, China;
2. School of Computer Science and Technology, Soochow University, Suzhou Jiangsu 215006, China

Received:2014-10-27 Revised:2015-01-05 Online:2015-04-10 Published:2015-04-08

基于自监督学习的维基百科家庭关系抽取

朱苏阳^1,2, 惠浩添^1,2, 钱龙华^1,2, 张民^1,2

1. 苏州大学自然语言处理实验室, 江苏苏州 215006;
2. 苏州大学计算机科学与技术学院, 江苏苏州 215006

通讯作者: 朱苏阳
作者简介:朱苏阳(1989-),男,江苏苏州人,硕士研究生,主要研究方向:信息抽取; 惠浩添(1991-),男,江苏徐州人,硕士研究生,主要研究方向:信息抽取; 钱龙华(1966-),男,江苏苏州人,副教授,CCF会员,主要研究方向:自然语言处理; 张民(1970-),男,黑龙江哈尔滨人,教授,博士生导师,CCF会员,主要研究方向:机器翻译。
基金资助:
国家自然科学基金资助项目(61373096, 90920004);江苏省高校自然科学研究重大项目(11KJA520003)。

Abstract

Abstract:

Traditional supervised relation extraction demands a large scale of manually annotated training data while semi-supervised learning suffers from low recall. A self-supervised learning based approach was proposed to extract personal family relationships. First, semi-structured information (family relation triples) was mapped to the free text in Chinese Wikipedia to automatically generate annotated training data. Then family relations between person entities were extracted from Wikipedia text with feature-based relation extraction method. The experimental results on a manually annotated test family network show that this method outperforms Bootstrapping with F1-measure of 77%, implying that self-supervised learning can effectively extract personal family relationships.

Key words: self-supervised learning, Wikipedia, semi-structured information, relation extraction

摘要：

传统有监督的关系抽取方法需要大量人工标注的训练语料,而半监督方法则召回率较低,对此提出了一种基于自监督学习来抽取人物家庭关系的方法。该方法首先将中文维基百科的半结构化信息——家庭关系三元组映射到自由文本中,从而自动生成已标注的训练语料;然后,使用基于特征的关系抽取方法从中文维基百科的文本中获取人物间的家庭关系。在一个人工标注的家庭关系网络测试集上的实验结果表明,该方法优于自举方法,其F1指数达到77%,说明自监督学习可以较为有效地抽取人物家庭关系。

关键词: 自监督学习, 维基百科, 半结构化信息, 关系抽取

CLC Number:

TP391

ZHU Suyang, HUI Haotian, QIAN Longhua, ZHANG Min. Family relation extraction from Wikipedia by self-supervised learning[J]. Journal of Computer Applications, 2015, 35(4): 1013-1016.

朱苏阳, 惠浩添, 钱龙华, 张民. 基于自监督学习的维基百科家庭关系抽取[J]. 计算机应用, 2015, 35(4): 1013-1016.

References

[1] ZHOU G, ZHANG M. Extracting relation information from text documents by exploring various types of knowledge [J]. Information Processing and Management, 2007, 43(4): 969-982.
[2] ZHOU G, QIAN L, FAN J. Tree kernel-based semantic relation extraction with rich syntactic and semantic information [J]. Information Science, 2010, 180(8): 1313-1325.
[3] OH J-H, UCHIMOTO K, TORISAWA K. Bilingual co-training for monolingual hyponymy-relation acquisition [C]// ACL 2009: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language. Stroudsburg: Association for Computational Linguistics, 2009: 432-440.
[4] ZHU Z. Weakly-supervised relation classification for information extraction [C]// Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2004: 581-588.
[5] ZHANG M, SU J, WANG D, et al. Discovering relations between named entities from a large raw corpus using tree similarity-based clustering [C]// Proceedings of the Second International Joint Conference on Natural Language Processing. Berlin: Springer-Verlag, 2005: 378-389.
[6] JING H, KAMBHATLA N, ROUKOS S. Extracting social networks and biographical facts from conversational speech transcripts [C]// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2007: 1040-1047.
[7] AGARWAL A, CORVALAN A, JENSEN J, et al. Social network analysis of Alice in Wonderland [C]// Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature. Stroudsburg: Association for Computational Linguistics, 2012: 88-96.
[8] GU J, HU Y, QIAN L, et al. Research on building family networks based on bootstrapping and coreference resolution [C]// NLPCC 2013: Proceedings of the Second CCF Conference on Natural Language Processing and Chinese Computing, CCIS 400. Berlin: Springer-Verlag, 2013:200-211.
[9] KOMACHI M, KUDO T, SHIMBO M, et al. Graph-based analysis of semantic drift in Espresso-like bootstrapping algorithms [C]// EMNLP 2008: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2008: 1011-1020.
[10] MINTZ M, BILLS S, SNOW R, et al. Distant supervision for relation extraction without labeled data [C]// ACL 2009: 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. Stroudsburg: Association for Computational Linguistics, 2009: 1003-1011.
[11] RIEDEL S, YAO L, McCALLUM A. Modeling relations and their mentions without labeled text [C]// Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin: Springer-Verlag, 2010: 148-163.
[12] HOFFMANN R, ZHANG C, LING X, et al. Knowledge-based weak supervision for information extraction of overlapping relations [C]// HLT 2011: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2011: 541-550.
[13] SURDEANU M, TIBSHIRANI J, NALLAPATI R, et al. Multi-instance multi-label learning for relation extraction [C]// EMNLP-CoNLL 2012: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2012: 455-465.
[14] WU F, WELD D. Open information extraction using Wikipedia[C]// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2010:118-127.
[15] WANG Z, LI Z, LI J, et al. Transfer learning based cross-lingual knowledge extraction for Wikipedia [C]// Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2013: 641-650.
[16] HSU C, CHANG C, LIN C. A practical guide to support vector classification [R]. Taipei: University of National Taiwan, 2003.
[17] PANTEL P, PENNACCHIOTTI M. Espresso: leveraging generic patterns for automatically harvesting semantic relations [C]// Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2006: 113-120.

Family relation extraction from Wikipedia by self-supervised learning

基于自监督学习的维基百科家庭关系抽取

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