基于远程监督的多因子人物关系抽取模型

doi:10.11959/j.issn.1000-436x.2018118

Abstract

Abstract:

Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data,a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was proposed.For training data generation,the data produced by distant supervision would be filtered by multiple instance learning and the method of TF-IDF-based relation keyword detecting,which tried to make the training data has the manual annotation quality.Furthermore,the model combined lexical and syntactic features to extract the effective relation feature vector from two angles of words and semantics for classifier.The experiment results on large scale real-world datasets show that the proposed model outperforms other relation extraction methods which based on distant supervision.

Key words: relation extraction, person entity relation, distant supervision, machine learning, natural language processing

CLC Number:

TP391

Yangchen HUANG,Yan JIA,Liang GAN,Jing XU,Jiuming HUANG,Zhonghe HE. Multi-factor person entity relation extraction model based on distant supervision[J]. Journal on Communications, 2018, 39(7): 103-112.

Figures/Tables 11

References 29

[1]	CHINCHOR N , MARSH E . Muc-7 information extraction task definition[C]// The Seventh Message Understanding Conference (MUC-7),Appendices. 1998: 359-367.
[2]	CHE W , LIU T , LI S . Automatic entity relation extraction[J]. Journal of Chinese Information Processing, 2005,19(2): 1-6.
[3]	刘绍毓, 李弼程, 郭志刚 ,等. 实体关系抽取研究综述[J]. 信息工程大学学报, 2016(5): 541-547.
	LIU S Y , LI B C , GUO Z G ,et al. Review of entity relation extraction[J]. Journal of Information Engineering University, 2016(5): 541-547.
[4]	APPELT D E , HOBBS J R , BEAR J ,et al. SRI International FASTUS system:MUC-6 test results and analysis[C]// The 6th Conference On Message Understanding. 1995: 237-248.
[5]	YANGARBER R , GRISHMAN R . NYU:description of the proteus/PET system as used for MUC-7 ST[C]// Seventh Message Understanding Conference (MUC-7). 1998.
[6]	AONE C , RAMOS S M . REES:a large-scale relation and event extraction system[C]// The Sixth Conference On Applied Natural Language Processing. 2000: 76-83.
[7]	KAMBHATLA N , . Combining lexical,syntactic,and semantic features with maximum entropy models for extracting relations[C]// The ACL 2004 On Interactive Poster And Demonstration Sessions. 2004:22.
[8]	GUO D Z , JIAN S , JIE Z ,et al. Exploring various knowledge in relation extraction[C]// The 43rd Annual Meeting on Association for Computational Linguistics. 2005: 427-434.
[9]	宁海燕 . 实体关系自动抽取技术的比较研究[D]. 哈尔滨:哈尔滨工业大学, 2010.
	NING H Y . Comparative study of automatic entity relation extraction[D]. Harbin:Harbin Institute of Technology, 2010.
[10]	WANG L , CAO Z , MELO D G ,et al. Relation classification via multi-level attention CNNS[C]// The 54th Annual Meeting of the Association for Computational Linguistics. 2016: 1298-1307.
[11]	KUMAR S . A survey of deep learning methods for relation extraction[J]. arXiv preprint,arXiv:1705.03645, 2017.
[12]	ZHOU P , SHI W , TIAN J ,et al. Attention-based bidirectional long short-term memory networks for relation classification[C]// The 54th Annual Meeting of the Association for Computational Linguistics. 2016: 207-212.
[13]	CARLSON A , BETTERIDGE J , KISIEL B ,et al. Toward an architecture for never-ending language learning[C]// AAAI. 2010:3.
[14]	刘锦文, 许静, 张利萍 ,等. 基于标签传播和主动学习的人物社会关系抽取[J]. 计算机工程, 2017,34(2): 234-240.
	LIU J W , XU J , ZHANG L P ,et al. Personal social relation extraction based on label propagation and active learning[J]. Computer Engineering, 2017,34(2): 234-240.
[15]	秦兵, 刘安安, 刘挺 . 无指导的中文开放式实体关系抽取[J]. 计算机研究与发展, 2015,52(5): 1029-1035.
	QIN B , LIU A A , LIU T . Unsupervised Chinese open entity relation extraction[J]. Journal of Computer Research and Development, 2015,52(5): 1029-1035.
[16]	GASHTEOVSKI K , GEMULLA R , DEL C L . MinIE:minimizing facts in open information extraction[C]// The 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 2630-2640.
[17]	BOLLACKER K , EVANS C , PARITOSH P ,et al. Freebase:a collaboratively created graph database for structuring human knowledge[C]// The 2008 ACM SIGMOD International Conference on Management of Data. 2008: 1247-1250.
[18]	AUER S , BIZER C , KOBILAROV G ,et al. Dbpedia:a nucleus for a web of open data[M]// The Semantic Web. 2007: 722-735.
[19]	SUCHANEK F M , KASNECI G , WEIKUM G . Yago:a core of semantic knowledge[C]// The 16th International Conference on World Wide Web. 2007: 697-706.
[20]	MINTZ M , BILLS S , SNOW R ,et al. Distant supervision for relation extraction without labeled data[C]// The Joint Conference of the 47th Annual Meeting of the ACL and The 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009: 1003-1011.
[21]	JI G , LIU K , HE S ,et al. Distant supervision for relation extraction with sentence-level attention and entity descriptions[C]// AAAI. 2017: 3060-3066.
[22]	PERSHINA M , MIN B , XU W ,et al. Infusion of labeled data into distant supervision for relation extraction[C]// The 52nd Annual Meeting of the Association for Computational Linguistics. 2014: 732-738.
[23]	潘云, 布勒布丽汗·伊沙巴依, 杨静 ,等. 利用中文在线资源的远程监督人物关系抽取[J]. 小型微型计算机系统, 2015,4:11.
	PAN Y , BULEHULIHAN.YISHABAY , YANG J ,et al. Distant supervised personal relation extraction using chinese online resource[J]. Journal of Chinese Mini-Micro Computer Systems, 2015,4:11.
[24]	黄蓓静, 贺樑, 杨静 . 远程监督人物关系抽取中的去噪研究[J]. 计算机应用与软件, 2017,34(7): 11-18.
	HUANG B J , HE L , YANG J . Research on noise reduction in distant supervised personal relation extraction[J]. Computer Application and Software, 2017,34(7): 11-18.
[25]	CRAVEN M , KUMLIEN J . Constructing biological knowledge bases by extracting information from text sources[C]// ISMB. 1999: 77-86.
[26]	SURDEANU M , TIBSHIRANI J , NALLAPATI R ,et al. Multi-instance multi-label learning for relation extraction[C]// The 2012 Joint Conference On Empirical Methods In Natural Language Processing And Computational Natural Language Learning. 2012: 455-465.
[27]	HOFFMANN R , ZHANG C , LING X ,et al. Knowledge-based weak supervision for information extraction of overlapping relations[C]// The 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. 2011: 541-550.
[28]	胡宝顺, 王大玲, 于戈 ,等. 基于句法结构特征分析及分类技术的答案提取算法[J]. 计算机学报, 2008,31(4): 662-676.
	HU B S , WANG D L , YU G ,et al. An answer extraction algorithm based on syntax structure feature parsing and classification[J]. Chinese Journal of Computers, 2008,31(4): 662-676.
[29]	郭喜跃, 何婷婷, 胡小华 ,等. 基于句法语义特征的中文实体关系抽取[J]. 中文信息学报, 2014,28(6): 183-189.
	GUO X Y , HE T T , HU X H ,et al. Chinese named entity relation extraction based on syntactic and semantic features[J]. Journal of Chinese Information Processing, 2014,28(6): 183-189.

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关系类型	编号	知识库中的三元组/个	训练集/个	测试集/个
夫妻	1	96 593	22 176	5 543
父子	2	75 304	19 938	4 984
母子	3	52 047	16 182	4 046
兄弟	4	80 691	10 947	2 735
姐妹	5	20 785	14 432	3 611

n	数目/个
1	238
2	296
3	317
4	331
5	342

特征	P	R	F₁
dis	61.3%	57.5%	59.3%
+order	65.1%	60.4%	62.2%
+vn	66.4%	60.7%	63.4%
+parsing_r	72.6%	64.1%	68.1%
+p_dis	77.6%	65.8%	71.2%
+context	79.1%	66.9%	72.5%

新关系	P	R	F₁
朋友	76.4%	63.1%	69.1%
同事	73.7%	61.2%	66.8%

研究方法	P	R	F₁
标签传播模型	69.3%	—	—
模式聚类模型	74.2%	60.6%	64.6%
本文模型	79.1%	66.9%	72.5%

Multi-factor person entity relation extraction model based on distant supervision

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