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Kernel methods for relation extraction

Authors:

Dmitry Zelenko,

Anthony RichardellaAuthors Info & Claims

EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10

Pages 71 - 78

https://doi.org/10.3115/1118693.1118703

Published: 06 July 2002 Publication History

Abstract

We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.

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EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10

July 2002

328 pages

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Association for Computational Linguistics

United States

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Published: 06 July 2002

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Cited By

Wang LAmrouch HHu JGarg SLin Y(2024)LLM-Assisted Analytics in Semiconductor Test (Invited)Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685974(1-7)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685974
Wei PHuang ZOuyang HHu QZeng BFeng GGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)CGI-MRE: A Comprehensive Genetic-Inspired Model For Multimodal Relation ExtractionProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658103(524-532)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658103
Dai QYang WWang LWei FTuo M(2024)SOIRPNeurocomputing10.1016/j.neucom.2024.127492580:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127492
Sun XGuo QGe S(2024)GFNKnowledge-Based Systems10.1016/j.knosys.2024.112137300:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112137
Shang WWang BZhu PDing LWang S(2024)A Span-based Multivariate Information-aware Embedding Network for joint relational triplet extraction of threat intelligenceKnowledge-Based Systems10.1016/j.knosys.2024.111829295:COnline publication date: 18-Jul-2024
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Liu BSong HLin YWeng XSu ZZhao XYang J(2024)PRTAComputers in Biology and Medicine10.1016/j.compbiomed.2024.108539176:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108539
Yan QDing RLu L(2023)RPnet: A Relation Perception based Entity-relation Extraction MethodProceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security10.1145/3661638.3661642(16-22)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3661638.3661642
Lin RFan JWu H(2023)Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information ExtractionACM Transactions on Intelligent Systems and Technology10.1145/362067514:6(1-18)Online publication date: 14-Nov-2023
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Zhong LWu JLi QPeng HWu X(2023)A Comprehensive Survey on Automatic Knowledge Graph ConstructionACM Computing Surveys10.1145/361829556:4(1-62)Online publication date: 5-Sep-2023
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Yu MChen YZhao MXu TYu JYu RLiu HLi X(2023)Semantic piecewise convolutional neural network with adaptive negative training for distantly supervised relation extractionNeurocomputing10.1016/j.neucom.2023.03.005537:C(12-21)Online publication date: 7-Jun-2023
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