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Volumn , Issue , 2008, Pages 2120-2124

Exploiting the role of position feature in Chinese relation extraction

Author keywords

[No Author keywords available]

Indexed keywords

EXTRACTION; NATURAL LANGUAGE PROCESSING SYSTEMS; SEMANTICS;

EID: 84906922593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (10)

References (14)
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    • Bunescu, R.1    Mooney, R.2
  • 2
    • 85144480129 scopus 로고    scopus 로고
    • Dependency tree kernels for relation extraction
    • Culotta A. and Sorensen J. (2004). Dependency Tree Kernels for Relation Extraction, in Proceedings of ACL, pages 423-429.
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    • Culotta, A.1    Sorensen, J.2
  • 3
    • 84858434398 scopus 로고    scopus 로고
    • A systematic exploration of the feature space for relation extraction
    • Jiang J. and Zhai C. (2007). A Systematic Exploration of the Feature Space for Relation Extraction. In proceedings of NAACL/HLT, pages 113-120.
    • (2007) Proceedings of NAACL/HLT , pp. 113-120
    • Jiang, J.1    Zhai, C.2
  • 4
    • 85140744814 scopus 로고    scopus 로고
    • Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations
    • Kambhatla N. (2004). Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Extracting Relations. In Proceedings of ACL, pages 178-181.
    • (2004) Proceedings of ACL , pp. 178-181
    • Kambhatla, N.1
  • 6
    • 84859889184 scopus 로고    scopus 로고
    • Exploring various knowledge in relation extraction
    • Zhou G., Su J., Zhang J., and Zhang M. (2005). Exploring Various Knowledge in Relation Extraction. In Proceedings of ACL, pages 427-434.
    • (2005) Proceedings of ACL , pp. 427-434
    • Zhou, G.1    Su, J.2    Zhang, J.3    Zhang, M.4
  • 7
    • 84860532294 scopus 로고    scopus 로고
    • A composite kernel to extract relations between entities with both flat and structured features
    • Zhang M., Zhang J., Su J. and Zhou G. (2006). A Composite Kernel to Extract Relations between Entities with both Flat and Structured Features, in Proceedings of COLING/ACL, pages 825-832.
    • (2006) Proceedings of COLING/ACL , pp. 825-832
    • Zhang, M.1    Zhang, J.2    Su, J.3    Zhou, G.4
  • 8
    • 0000636553 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • Joachims T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. In Proceedings of European Conference on Machine Learning.
    • (1998) Proceedings of European Conference on Machine Learning
    • Joachims, T.1
  • 10
    • 80053360686 scopus 로고    scopus 로고
    • Tree kernel-based relation extraction with context-sensitive structured parse tree information
    • Zhou G., Zhang, M, Ji D. and Zhu Q. (2007). Tree Kernel-based Relation Extraction with Context-Sensitive Structured Parse Tree Information. In Proceedings of EMNLP, pages 728-736.
    • (2007) Proceedings of EMNLP , pp. 728-736
    • Zhou, G.1    Zhang, M.2    Ji, D.3    Zhu, Q.4
  • 11
    • 27144549260 scopus 로고    scopus 로고
    • Editorial: Special issue on learning from imbalanced datasets
    • Chawla N., Japkowicz N. and Kolcz A. (2004) "Editorial: Special Issue on Learning from Imbalanced Datasets", SIGKDD Explorations, 6(1):1-6.
    • (2004) SIGKDD Explorations , vol.6 , Issue.1 , pp. 1-6
    • Chawla, N.1    Japkowicz, N.2    Kolcz, A.3
  • 12
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    • Learning from imbalanced data sets: A comparison of various strategies
    • Papers from, Tech. rep, Menlo Park, CA: AAAI Press
    • Japkowicz, N. (2000). Learning from imbalanced data sets: a comparison of various strategies. In Papers from the AAAI Workshop on Learning from Imbalanced Data Sets. Tech. rep. WS-00-05, Menlo Park, CA: AAAI Press.
    • (2000) The AAAI Workshop on Learning from Imbalanced Data Sets
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  • 13
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    • Integrating probabilistic extraction models and data mining to discover relations and patterns in text
    • Culotta A. McCallum A, Betz J. (2006) Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text. HLT-NAACL, pages 296-303
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  • 14
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    • Minority vote: At-least-n voting improves recall for extracting relations
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    • Kambhatla, N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.