Ontology of human relation extraction based on dependency syntax rules
Authors: 
Long He, Likun QiuAuthors Info & Claims
Pages 1151 - 1157
Abstract
This paper proposed a novel scheme for extracting character relation from unstructured text based on dependency grammar rules. First of all, we took the Three Kingdoms characters as our research object, then selected articles containing target relationships and thus constructed a corpus consisting of 1000 sentences. Secondly, We analyzed the corpus and developed a set of dependent grammar rules for relation extraction. Finally, we proposed a system, which makes it possible for computers to automatically extract and identify character relationships.
References
[1]
Wang Yuan, Xu Dezhi, Chen Jianer. Research on entity relation extraction of complex Chinese text (in Chinese){J}.computer science, 2009, 36 (8), 208--211.
[2]
D.McClosky, M.Surdeanu, C.D.Manning. Event Extraction as Dependency Parsing. Proceedings of ACL-HLT, 2011.
[3]
Qiu Likun, Zhang Yue, Jin Peng, et al. Multi-view Chinese treebanking// Proceedings of COLING. Dublin, 2014, 257--268.
[4]
Liang Xiujuan. A review of scientific knowledge mapping (in Chinese){J}.Library magazine, 2009 (6), 58--62.
[5]
Grishman R, Sundheim B. Message Understanding Conference-6: A Brief History.{C}// Conference on Computational Linguistics.1996:466--471.
[6]
Zhu Dexi.Lectures on grammar (in Chinese). BeiJing: The Commercial Press, 2003.
[7]
Chen Baoya. Methodology of Chinese Linguistics in the twentieth Century (in Chinese) : Shandong Education Press, 1999.
[8]
Zhang Min, Zhou Guodong and Aw Aiti. Exploring syntactic structured features over parse trees for relation extraction using kernel methods. Information Processing and Management. 2008 (44), 687--701.
[9]
O.Etzioni, M.Bnnko, S.Soderland. Open Information Extraction from the Web. Communication of the ACM. 2008, 51 (12), 2--7.
[10]
J.Cowie, W.Lerman, S.Minton, et al. Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach. Data Engineering Bulletin, 2000, 23 (4), 33--41.
- Ontology of human relation extraction based on dependency syntax rules
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Information
Published In
August 2017
1284 pages
ISBN:9781450349512
DOI:10.1145/3106426
- Conference Chair:
- Amit Sheth,
- General Chairs:
- Axel Ngonga,
- yin Wang,
- Elizabeth Chang,
- Dominik Ślęzak,
- Bogdan Franczyk,
- Program Chairs:
- Rainer Alt,
- Xiaohui Tao
Copyright © 2017 ACM.
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Sponsors
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
- Web Intelligence Consortium
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 August 2017
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- Research-article
Funding Sources
- National Natural Science Foundation of China
Conference
WI '17
Sponsor:
- SIGAI
- TCII
Acceptance Rates
WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
Overall Acceptance Rate 118 of 178 submissions, 66%
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