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Kernel methods for relation extraction

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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Published In

cover image DL Hosted proceedings
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

Publication History

Published: 06 July 2002

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Overall Acceptance Rate 73 of 234 submissions, 31%

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  • (2025)Improved relation extraction through key phrase identification using community detection on dependency treesComputer Speech and Language10.1016/j.csl.2024.10170689:COnline publication date: 1-Jan-2025
  • (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
  • (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
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  • (2024)Joint multimodal entity-relation extraction based on temporal enhancement and similarity-gated attentionKnowledge-Based Systems10.1016/j.knosys.2024.112504304:COnline publication date: 25-Nov-2024
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  • (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
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