Please wait a minute...
Advanced Search
现代图书情报技术  2013, Vol. 29 Issue (11): 30-39    DOI: 10.11925/infotech.1003-3513.2013.11.05
  知识组织与知识管理 本期目录 | 过刊浏览 | 高级检索 |
关系抽取技术研究综述
黄勋, 游宏梁, 于洋
中国国防科技信息中心 北京 100142
A Review of Relation Extraction
Huang Xun, You Hongliang, Yu Yang
China Defense Science & Technology Information Center, Beijing 100142, China
全文: PDF(6206 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 对关系抽取技术研究概况进行总结。在回顾关系抽取发展历史的基础上,将关系抽取研究划分为两个阶段:面向特定领域的关系抽取研究和面向开放互联网文本的关系抽取研究。在分析相关文献的基础上,总结出两个研究阶段的技术路线:面向特定领域的关系抽取技术以基于标注语料的机器学习方法为主;面向开放互联网文本的关系抽取则根据不同任务需要,采取基于启发式规则的方法或者基于背景知识库实例的机器学习方法。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
于洋
黄勋
游宏梁
关键词 关系抽取信息抽取机器学习    
Abstract:The paper summarizes the research of relation extraction techonology. It firstly gives a brief overview of relation extraction,and divides the research into two phases: the relation extraction in specific domains and the relation extraction of Web text. Then,analyzes the major methodologies of the two phases: the relation extraction in specific domains mainly uses machine learning methods with annotated corpora, while the relation extraction of Web text uses rule-based methods or distant supervision methods according to different demands.
Key wordsRelation extraction    Information extraction    Machine learning
收稿日期: 2013-07-12     
:  TP391  
引用本文:   
黄勋, 游宏梁, 于洋. 关系抽取技术研究综述[J]. 现代图书情报技术, 2013, 29(11): 30-39.
Huang Xun, You Hongliang, Yu Yang. A Review of Relation Extraction. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2013.11.05.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2013.11.05
[1] Message Understanding Conference[EB/OL].[2013-06-24]. http://en.wikipedia.org/wiki/Message_Understanding_Conference.
[2] MUC-7 Information Extraction Task Definition[EB/OL].[2013-06-24]. http://www.itl.nist.gov/iaui/894.02/related_projects/muc/proceedings/ie_task.html.
[3] Automatic Content Extraction(ACE) Evaluation[EB/OL].[2013-06-24]. http://www.itl.nist.gov/iad/mig//tests/ace/.
[4] The ACE 2007(ACE2007) Evaluation Plan[EB/OL].[2013-06-24]. http://www.itl.nist.gov/iad/mig//tests/ace/ace07/doc/ace07-evalplan.v1.3a.pdf.
[5] Knowledge Base Population (KBP) 2013 [EB/OL].[2013-06-24]. http://www.nist.gov/tac/2013/KBP/.
[6] Mintz M, Bills S, Snow R, et al. Distant Supervision for Relation Extraction Without Labeled Data[C].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.Association for Computational Linguistics,2009:1003-1011.
[7] Banko M. Open Information Extraction for the Web[D]. University of Washington,2009.
[8] Automatic Content Extraction 2008 Evaluation Plan (ACE08)[EB/OL].[2013-08-24]. http://www.itl.nist.gov/iad/mig/tests/ace/2008/doc/ace08-evalplan.v1.2d.pdf.
[9] Aone C, Halverson L, Hampton T, et al. SRA: Description of the IE2 System Used for MUC-7[C].In: Proceedings of the 7th Message Understanding Conference (MUC-7). 1998.
[10] Fukumoto F, Shimohata M, Masui F, et al. Oki Electric Industry: Description of the Oki System as Used for MET-2[C].In: Proceedings of the 7th Message Understanding Conference.1998.
[11] Humphreys K, Gaizauskas R, Azzam S, et al. University of Sheffield: Description of the LaSIE-II System as Used for MUC-7[C].In: Proceedings of the 7th Message Understanding Conference (MUC-7). 1998.
[12] Miller S, Fox H, Ramshaw L, et al. A Novel Use of Statistical Parsing to Extract Information from Text[C].In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference. Association for Computational Linguistics, 2000: 226-233.
[13] Kambhatla N. Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Extracting Relations[C].In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions. Stroudsburg, PA, USA: Association for Computational Linguistics, 2004.
[14] 车万翔, 刘挺, 李生. 实体关系自动抽取[J]. 中文信息学报, 2005,19(2): 1-6.(Che Wanxiang, Liu Ting, Li Sheng. Automatic Entity Relation Extraction[J].Journal of Chinese Information Processing, 2005,19(2):1-6.)
[15] Zhou G D, Su J, Zhang J, et al. Exploring Various Knowledge in Relation Extraction[C].In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics,2005:427-434.
[16] Jiang J, Zhai C X. A Systematic Exploration of the Feature Space for Relation Extraction[C].In: Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT'07). 2007:113-120.
[17] 董静, 孙乐, 冯元勇, 等. 中文实体关系抽取中的特征选择研究[J]. 中文信息学报, 2007,21(4): 80-85.( Dong Jing, Sun Le, Feng Yuanyong, et al. Chinese Automatic Entity Relation Extraction[J]. Journal of Chinese Information Processing, 2007, 21(4): 80-85.)
[18] 陈宇, 郑德权, 赵铁军. 基于 Deep Belief Nets 的中文名实体关系抽取[J]. 软件学报, 2012,23(10): 2572-2585.(Chen Yu, Zheng Dequan, Zhao Tiejun.Chinese Relation Extraction Based on Deep Belief Nets[J].Journal of Software, 2012,23(10): 2572-2585.)
[19] Zelenko D, Aone C, Richardella A. Kernel Methods for Relation Extraction[J]. The Journal of Machine Learning Research, 2003,3:1083-1106.
[20] Culotta A, Sorensen J. Dependency Tree Kernels for Relation Extraction[C].In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2004.
[21] Bunescu R C, Mooney R J. A Shortest Path Dependency Kernel for Relation Extraction[C].In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics,2005: 724-731.
[22] 黄瑞红, 孙乐, 冯元勇, 等. 基于核方法的中文实体关系抽取研究[J]. 中文信息学报, 2008, 22(5): 102-108.(Huang Ruihong, Sun Le, Feng Yuanyong, et al. A Study on Kernel-based Chinese Relation Extraction[J]. Journal of Chinese Information Processing, 2008, 22(5):102-108.)
[23] Zhang M, Zhang J, Su J, et al. A Composite Kernel to Extract Relations Between Entities with Both Flat and Structured Features[C].In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2006: 825-832.
[24] Zhou G D, Zhang M, Ji D H, et al. Tree Kernel-based Relation Extraction with Context-Sensitive Structured Parse Tree Information[C].In: Proceedings of the 2007 Joint Conference on Empirical ethods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL-2007). 2007:728-736.
[25] Qian L H, Zhou G D, Kong F, et al.Exploiting Constituent Dependencies for Tree Kernel-based Semantic Relation Extraction[C].In: Proceedings of the 22nd International Conference on Computational Linguistics. Association for Computational Linguistics, 2008:697-704.
[26] 庄成龙, 钱龙华, 周国栋. 基于树核函数的实体语义关系抽取方法研究[J]. 中文信息学报, 2009, 23(1): 3-9.(Zhuang Chenglong, Qian Longhua, Zhou Guodong. Research on Tree Kernel-based Entity Semantic Relation Extraction[J]. Journal of Chinese Information Processing, 2009, 23(1): 3-9.)
[27] 虞欢欢, 钱龙华, 周国栋, 等. 基于合一句法和实体语义树的中文语义关系抽取[J]. 中文信息学报, 2010, 24(5): 17-23.(Yu Huanhuan, Qian Longhua, Zhou Guodong, et al. Chinese Semantic Relation Extraction Based on Unified Syntactic and Entity Semantic Tree[J]. Journal of Chinese Information Processing, 2010,24(5):17-23.)
[28] 刘克彬, 李芳, 刘磊, 等. 基于核函数中文关系自动抽取系统的实现[J]. 计算机研究与发展, 2007, 44(8):1406-1411.(Liu Kebin, Li Fang, Liu Lei, et al. Implementation of a Kernel-based Chinese Relation Extraction System[J].Journal of Computer Research and Development, 2007, 44(8): 1406-1411.)
[29] Brin S. Extracting Patterns and Relations from the World Wide Web[C]. In: Proceedings of International Workshop on the World Wide Web and Databases. London, UK: Springer-Verlag, 1999: 172-183.
[30] Agichtein E, Gravano L. Snowball: Extracting Relations from Large Plain-text Collections[C]. In: Proceedings of the 5th ACM Conference on Digital Libraries. ACM, 2000:85-94.
[31] Etzioni O, Cafarella M, Downey D, et al. Unsupervised Named-entity Extraction from the Web: An Experimental Study[J]. Artificial Intelligence, 2005,165(1): 91-134.
[32] Rosenfeld B, Feldman R. URES: An Unsupervised Web Relation Extraction System[C].In: Proceedings of the COLING/ACL on Main Conference Poster Sessions. Association for Computational Linguistics,2006:667-674.
[33] Feldman R, Rosenfeld B. Boosting Unsupervised Relation Extraction by Using NER[C].In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2006:473-481.
[34] 何婷婷, 徐超, 李晶, 等. 基于种子自扩展的命名实体关系抽取方法[J]. 计算机工程, 2006, 32(21): 183-184,193.(He Tingting, Xu Chao, Li Jing, et al. Named Entity Relation Extraction Method Based on Seed Self-expansion[J].Computer Engineering, 2006, 32(21):183-184,193.)
[35] 李维刚, 刘挺, 李生. 基于网络挖掘的实体关系元组自动获取[J]. 电子学报, 2007,35(11): 2111-2116.(Li Weigang, Liu Ting, Li Sheng. Automated Entity Relation Tuple Extraction Using Web Mining[J].Acta Electronica Sinica, 2007, 35(11): 2111-2116.)
[36] Xu F Y, Uszkoreit H, Li H. A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity[C]. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.2007: 584-591.
[37] Xu F Y. Bootstrapping Relation Extraction from Semantic Seeds[D]. Saarland University, 2008.
[38] Xu F Y, Uszkoreit H, Li H, et al. Adaptation of Relation Extraction Rules to New Domains[C].In: Proceedings of the Poster Session of the 6th International Conference on Language Resources and Evaluation(LREC'08).2008.
[39] Xu F Y, Uszkoreit H, Krause S, et al. Boosting Relation Extraction with Limited Closed-world Knowledge[C].In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters.Association for Computational Linguistics, 2010:1354-1362.
[40] Zhu J, Nie Z, Liu X J, et al. StatSnowball: A Statistical Approach to Extracting Entity Relationships[C].In: Proceedings of the 18th International Conference on World Wide Web. ACM, 2009:101-110.
[41] Carlson A, Betteridge J, Wang R C, et al. Coupled Semi-supervised Learning for Information Extraction[C].In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 2010:101-110.
[42] 陈锦秀, 姬东鸿. 基于图的半监督关系抽取[J]. 软件学报,2008, 19(11): 2843-2852.(Chen Jinxiu, Ji Donghong. Graph-based Semi-Supervised Relation Extraction[J].Journal of Software, 2008, 19(11):2843-2852.)
[43] Curran J R, Murphy T, Scholz B. Minimising Semantic Drift with Mutual Exclusion Bootstrapping[C].In: Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics.2007:172-180.
[44] Hasegawa T, Sekine S, Grishman R. Discovering Relations Among Named Entities from Large Corpora[C].In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004.
[45] Stevenson M. An Unsupervised WordNet-based Algorithm for Relation Extraction[C]. In: Proceedings of the 4th International Conference on Language Resources and Evaluation Workshop “Beyond Named Entity: Semantic Labelling for NLP Tasks”, Lisbon, Portugal.2004.
[46] Zhang M, Su J, Wang D, et al. Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-based Clustering[C].In: Proceedings of the 2nd International Joint Conference on Natural Language Processing(IJCNLP'05). Berlin, Heidelberg: Springer-Verlag, 2005: 378-389.
[47] Rosenfeld B, Feldman R. Clustering for Unsupervised Relation Identification[C].In: Proceedings of the 16th ACM Conference on Information and Knowledge Management. ACM, 2007: 411-418.
[48] Davidov D, Rappoport A, Koppel M. Fully Unsupervised Discovery of Concept-specific Relationships by Web Mining[C]. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.2007:232-239.
[49] Yan Y, Okazaki N, Matsuo Y, et al. Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web[C].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. Association for Computational Linguistics,2009:1021-1029.
[50] Bollegala D T, Matsuo Y, Ishizuka M. Measuring the Similarity Between Implicit Semantic Relations from the Web[C].In: Proceedings of the 18th International Conference on World Wide Web. ACM, 2009:651-660.
[51] Bollegala D T, Matsuo Y, Ishizuka M. Relational Duality: Unsupervised Extraction of Semantic Relations Between Entities on the Web[C].In: Proceedings of the 19th International Conference on World Wide Web. ACM, 2010:151-160.
[52] Sekine S.On-Demand Information Extraction[C].In: Proceedings of the COLING/ACL on Main Conference Poster Sessions. Association for Computational Linguistics, 2006:731-738.
[53] Shinyama Y, Sekine S.Preemptive Information Extraction Using Unrestricted Relation Discovery[C].In: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Association for Computational Linguistics,2006:304-311.
[54] Yates A, Cafarella M, Banko M, et al. TextRunner: Open Information Extraction on the Web[C].In: Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. Association for Computational Linguistics,2007:25-26.
[55] Wu F, Weld D. Autonomously Semantifying Wikipedia[C]. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM'07), Lisbon, Portugal. 2007:41-50.
[56] Etzioni O, Fader A, Christensen J, et al. Open Information Extraction: The 2nd Generation[C].In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. AAAI Press, 2011:3-10.
[57] Schmitz M, Bart R, Soderland S, et al. Open Language Learning for Information Extraction[C].In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 2012: 523-534.
[58] Ji H, Grishman R. Knowledge Base Population: Successful Approaches and Challenges[C].In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.2011:1148-1158.
[59] Mintz M, Bills S, Snow R, et al. Distant Supervision for Relation Extraction Without Labeled Data[C].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.Association for Computational Linguistics,2009:1003-1011.
[60] Krause S, Li H, Uszkoreit H, et al. Large-scale Learning of Relation-extraction Rules with Distant Supervision from the Web[C]. In: Proceedings of the 11th International Semantic Web Conference, Boston, MA, USA. Berlin,Heidelberg: Springer, 2012:263-278.
[61] Akbik A, Visengeriyeva L, Herger P, et al. Unsupervised Discovery of Relations and Discriminative Extraction Patterns[C]. In: Proceedings of COLING.2012:17-32.
[62] Nguyen T V T, Moschitti A. End-to-End Relation Extraction Using Distant Supervision from External Semantic Repositories[C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.2011:277-282.
[63] Krishnamurthy R, Li Y Y, Raghavan S, et al. SystemT: A System for Declarative Information Extraction[J]. ACM SIGMOD Record,2009,37(4):7-13.
[64] Cunningham H, Maynard D, Tablan V,et al. JAPE: A Java Annotation Patterns Engine[OL]. [2013-06-24].http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=B038120DE79C13635 419187BFF58DFFF?doi=10.1.1.32.3929&rep=rep1&type=pdf.
[65] Li Y Y, Chiticariu L, Yang H, et al. WizIE: A Best Practices Guided Development Environment for Information Extraction[C].In: Proceedings of the ACL 2012 System Demonstrations. Association for Computational Linguistics,2012:109-114.
[66] Chiticariu L, Krishnamurthy R, Li Y Y, et al. Domain Adaptation of Rule-based Annotators for Named-entity Recognition Tasks[C].In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics,2010:1002-1012.
[67] Read the Web[EB/OL].[2013-07-07]. http://rtw.ml.cmu.edu/rtw/.
[1] 胡佳慧,方安,赵琬清,杨晨柳,任慧玲. 面向知识发现的中文电子病历标注方法
研究 *
[J]. 数据分析与知识发现, 2019, 3(7): 123-132.
[2] 张金柱,胡一鸣. 融合表示学习与机器学习的专利科学引文标题自动抽取研究*[J]. 数据分析与知识发现, 2019, 3(5): 68-76.
[3] 吴粤敏,丁港归,胡滨. 基于注意力机制的农业金融文本关系抽取研究*[J]. 数据分析与知识发现, 2019, 3(5): 86-92.
[4] 刘志强,都云程,施水才. 基于改进的隐马尔科夫模型的网页新闻关键信息抽取*[J]. 数据分析与知识发现, 2019, 3(3): 120-128.
[5] 徐红霞,李春旺. 科技文献内容知识点抽取研究综述[J]. 数据分析与知识发现, 2019, 3(3): 14-24.
[6] 张紫玄,王昊,朱立平,邓三鸿. 中国海关HS编码风险的识别研究*[J]. 数据分析与知识发现, 2019, 3(1): 72-84.
[7] 刘丽娜,齐佳音,张镇平,曾丹. 品牌对商品在线销量的影响*——基于海量商品评论的在线声誉和品牌知名度的调节作用研究[J]. 数据分析与知识发现, 2018, 2(9): 10-21.
[8] 牟冬梅,金姗,琚沅红. 基于文献数据的疾病与基因关联关系研究*[J]. 数据分析与知识发现, 2018, 2(8): 98-106.
[9] 贾隆嘉,张邦佐. 高校网络舆情安全中主题分类方法研究*——以新浪微博数据为例[J]. 数据分析与知识发现, 2018, 2(7): 55-62.
[10] 陆伟,罗梦奇,丁恒,李信. 深度学习图像标注与用户标注比较研究*[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[11] 王丽,邹丽雪,刘细文. 基于LDA主题模型的文献关联分析及可视化研究[J]. 数据分析与知识发现, 2018, 2(3): 98-106.
[12] 范馨月,崔雷. 基于网络属性的抗肿瘤药物靶点预测方法及其应用*[J]. 数据分析与知识发现, 2018, 2(12): 98-108.
[13] 赵杨,袁析妮,陈亚文,武立强. 基于机器学习混合算法的APP广告转化率预测研究*[J]. 数据分析与知识发现, 2018, 2(11): 2-9.
[14] 王欣,冯文刚. 在线极端主义和激进化监测技术综述*[J]. 数据分析与知识发现, 2018, 2(10): 2-8.
[15] 张琴,郭红梅,张智雄. 融合词嵌入表示特征的实体关系抽取方法研究*[J]. 数据分析与知识发现, 2017, 1(9): 8-15.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn