Please wait a minute...
New Technology of Library and Information Service  2008, Vol. 24 Issue (8): 18-23    DOI: 10.11925/infotech.1003-3513.2008.08.03
Current Issue | Archive | Adv Search |
Review on Techniques of Entity Relation Extraction
Xu Jian1,2,3  Zhang ZhixiongWu Zhenxin1
1 (National Science Library, Chinese Academy of Sciences, Beijing 100190, China)
2(Graduate University of the Chinese Academy of Sciences, Beijing 100049,China)
3(Department of Information Management,Sun Yat-Sen University, Guangzhou 510275,China)
Download: PDF(373 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

 Entity relation extraction is a very important task in text information extraction domain. It first summarizes the development of entity relation extraction related to MUC and ACE, and then points out that main difficulties exist in the process of relation extraction are acquisition of training dataset, acquisition of templates, and co-reference resolution. Based on the analysis of recent related literatures, systems and projects, it concludes the entity relation extraction methods as follows:templates method, lexicon driven method, machine learning method, Ontology driven method, and hybrid method. The analysis of these methods can help to build more efficient entity relation extraction system in further step.

Key wordsEntity relation extraction      Information extraction      Relation extraction methods     
Received: 16 June 2008      Published: 25 August 2008
: 

G250.73

 
Corresponding Authors: Xu Jian     E-mail: xujian@mail.las.ac.cn
About author:: Xu Jian,Zhang Zhixiong,Wu Zhenxin

Cite this article:

Xu Jian,Zhang Zhixiong,Wu Zhenxin. Review on Techniques of Entity Relation Extraction. New Technology of Library and Information Service, 2008, 24(8): 18-23.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.08.03     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I8/18

[1] Schutz A, Buitelaar P. RelExt:A Tool for Relation Extraction from Text in Ontology Extension[C]. 4th International Semantic Web Conference, Galway, Ireland, November 6-10, 2005:593-606.
[2] Katrenko S,  Adriaans P. Learning Relations from Biomedical Corpora Using Dependency Tree Levels[C]. In:Proc. BENELEARN conference(2006), 2006.
[3] Relationship Extraction[EB/OL]. [2008-05-30]. http://en.wikipedia.org/wiki/Relationship_extraction.
[4] The ACE 2004 Evaluation Plan[EB/OL]. [2008-05-30]. http://www.nist.gov/speech/tests/ace/2004/doc/ace04-evalplan-v7.pdf.
[5] Automatic Content Extraction 2008 Evaluation Plan (ACE08)[EB/OL]. [2008-05-30]. http://www.nist.gov/speech/tests/ace/2008/doc/ace08-evalplan.v1.2.pdf.
[6] MUC[EB/OL]. [2008-05-30]. http://www.itl.nist.gov/iaui/894.02/related_projects/muc/.
[7] ACE[EB/OL]. [2008-05-30]. http://www.nist.gov/speech/tests/ace/.
[8] ACE08 Annotation Tasks[EB/OL]. [2008-05-30]. http://projects.ldc.upenn.edu/ace/annotation/.
[9] 邓擘, 樊孝忠, 杨立公. 用语义模式提取实体关系的方法[J]. 计算机工程, 2007,33(10):212-214.
[10] 姜吉发, 王树西. 一种自举的二元关系和二元关系模式获取方法[J]. 中文信息学报, 2005,19(2):71-77.
[11] 顾雪峰. 基于动态粒度思想的实体关系识别方法研究[EB/OL]. [2008-05-30]. http://www.cnki.com.cn/grid20/Detail.aspx.
[12] 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.
[13] 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.
[14] Zhang Y M, Zhou J F. A Trainable Method for Extracting Chinese Entity Names and Their Relations[C]. In: Proceedings of the Second Chinese Language Processing Workshop, Hong Kong,  2000:66-72.
[15] Appelt D E, Hobbs J R,  Bear J, et al. SRI International FASTUS System:MUC-6 Test Results and Analysis[C]. In: Proceedings of the 6th Message Understanding Conference (MUC-6),  1995:237-248.
[16] Roman Y, Grishman R. NYU:Description of the Proteus/PET System as Used for MUC-7 ST[C]. In: Proceedings of the 6th Message Understanding Conference (MUC-7), 1998.
[17] Aone C, Ramos2Santacruz M. Rees:A large-scale relation and event extraction system[C]. In:Proc of the 6th Applied Natural Language Processing Conference, New York, 2000:76-83.
[18] Zhang Y,  Zhou J F. A Trainable Method for Extracting Chinese Entity Names and Their Relations[C]. In: Proceedings of the second Chinese Language Processing Workshop, ACL, 2000:66-72.
[19]  Zhu Z. Weakly-supervised Relation Classification for Information Extraction[C]. In:Proceedings of the Thirteenth ACM conference on Information and Knowledge Management, Washington D.C., 2004:581-588.
[20] Banko M, Cafarella M J, Soderland S, et al. Open Information Extraction from the Web[C]. In: Proceeding of the International Joint Conferences on Artificial Intelligence, 2007.
[21] Iria J. T-Rex:A Flexible Relation Extraction Framework[C]. In: Proceeding of the 8th Annual Colloquium for the UK Special Interest Group for Computational Linguistics (CLUK’05), Manchester, January 2005.
[22] Iria, Mr. José, Ciravegna, Fabio. Relation Extraction for Mining the Semantic Web[C]. In: Proceedings Machine Learning for the Semantic Web Dagstuhl Seminar 05071, Dagstuhl, 2005.
[23] Sabou M, Mathieu d’Aquin, Motta E. SCARLET:SemantiC relAtion DiscoveRy by Harvesting onLinE onTologies[C]. In: Proceedings of the 5th European Semantic Web Conference, June, 2008.
[24] Specia L, Motta E. A Hybrid Approach for Extracting Semantic Relations from Texts[EB/OL]. [2008-05-30]. http://www.dcs.shef.ac.uk/~lucia/publications/SpeciaMotta_OLP2-2006.pdf.

[1] Zhiqiang Liu,Yuncheng Du,Shuicai Shi. Extraction of Key Information in Web News Based on Improved Hidden Markov Model[J]. 数据分析与知识发现, 2019, 3(3): 120-128.
[2] Dongmei Mu,Shan Jin,Yuanhong Ju. Finding Association Between Diseases and Genes from Literature Abstracts[J]. 数据分析与知识发现, 2018, 2(8): 98-106.
[3] Yufeng Duan,Sisi Huang. Information Extraction from Chinese Plant Species Diversity Description Text[J]. 现代图书情报技术, 2016, 32(1): 87-96.
[4] Liu Wei, Wang Xing, Song Peiyan. A Noise Cleaning Method for Synonym Extraction Results[J]. 现代图书情报技术, 2015, 31(6): 64-70.
[5] Jiang Chuntao. Automatic Annotation of Bibliographical References in Chinese Patent Documents[J]. 现代图书情报技术, 2015, 31(10): 81-87.
[6] Li Xiangdong, Huo Yayong, Huang Li. Study of Book Pages Automatic Identification and Bibliographic Information Extraction[J]. 现代图书情报技术, 2014, 30(4): 71-77.
[7] Liu Yajing, Wang Yanxi, Hao Dan, Zhou Jinhui. Study on the Methods of Institutional Repository Supporting Research Services[J]. 现代图书情报技术, 2014, 30(3): 1-7.
[8] Zhang Han, Liu Shuangmei. Comparative Analysis of Centrality Indices in Extracting Concepts from Semantic Predication Network——Based on Disease Treatment Research[J]. 现代图书情报技术, 2013, (6): 30-35.
[9] Huang Xun, You Hongliang, Yu Yang. A Review of Relation Extraction[J]. 现代图书情报技术, 2013, 29(11): 30-39.
[10] He Lin, He Juan, Shen Gengyu, Yang Bo, Huang Shuiqing. An Approach to Discovery of Reference Control Gene for qRT-PCR Experiment Based on Texting Mining[J]. 现代图书情报技术, 2012, 28(7): 109-114.
[11] Gao Qiang, You Hongliang. Study on Named Entity Recognition Based on Cascaded Model for Field of Defense[J]. 现代图书情报技术, 2012, (11): 47-52.
[12] Wang Xiuyan, Cui Lei. Overview of Semantic Relations Extraction Between Biomedical Entities by Key Verbs[J]. 现代图书情报技术, 2011, 27(9): 21-27.
[13] Zhou Hong, Zhang Bei, Jiang Airong, Zhang Chengyu. Design and Implementation of Library Bibliography Information Self SMS Push Service[J]. 现代图书情报技术, 2011, 27(7/8): 127-131.
[14] Wang Zhichao, Weng Nan, Wang Yu. Research of Title Party News Identification Technology Based on Topic Sentence Similarity[J]. 现代图书情报技术, 2011, (11): 48-53.
[15] Lu Wanhui, Ma Jianxia. Research on Complex Time Information Extraction Based on CRF Model[J]. 现代图书情报技术, 2011, 27(10): 29-33.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn