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Convolution kernels on constituent, dependency and sequential structures for relation extraction

Authors:

Truc-Vien T. Nguyen,

Alessandro Moschitti,

Giuseppe RiccardiAuthors Info & Claims

EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3

Pages 1378 - 1387

Published: 06 August 2009 Publication History

Abstract

This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. We investigate the effectiveness of such representations in the automated relation extraction from texts. We process the above data by means of Support Vector Machines along with the syntactic tree, the partial tree and the word sequence kernels. Our study on the ACE 2004 corpus illustrates that the combination of the above kernels achieves high effectiveness and significantly improves the current state-of-the-art.

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Cited By

Taghizadeh NFaili H(2021)Cross-lingual Adaptation Using Universal DependenciesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/344825120:4(1-23)Online publication date: 26-May-2021
https://dl.acm.org/doi/10.1145/3448251
Ma FLi YZhang CGao JDu NFan W(2019)MCVAE: Margin-based Conditional Variational Autoencoder for Relation Classification and Pattern GenerationThe World Wide Web Conference10.1145/3308558.3313436(3041-3048)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313436
Zheng SXu JZhou PBao HQi ZXu B(2016)A neural network framework for relation extractionKnowledge-Based Systems10.1016/j.knosys.2016.09.019114:C(12-23)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.09.019
Show More Cited By

Index Terms

Convolution kernels on constituent, dependency and sequential structures for relation extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
2. Information systems
  1. Information retrieval

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cover image DL Hosted proceedings

EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3

August 2009

573 pages

ISBN:9781932432633

Program Chairs:
Philipp Koehn
University of Edinburgh
,
Rada Mihalcea
University of North Texas

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 06 August 2009

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Overall Acceptance Rate 73 of 234 submissions, 31%

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Cited By

Taghizadeh NFaili H(2021)Cross-lingual Adaptation Using Universal DependenciesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/344825120:4(1-23)Online publication date: 26-May-2021
https://dl.acm.org/doi/10.1145/3448251
Ma FLi YZhang CGao JDu NFan W(2019)MCVAE: Margin-based Conditional Variational Autoencoder for Relation Classification and Pattern GenerationThe World Wide Web Conference10.1145/3308558.3313436(3041-3048)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313436
Zheng SXu JZhou PBao HQi ZXu B(2016)A neural network framework for relation extractionKnowledge-Based Systems10.1016/j.knosys.2016.09.019114:C(12-23)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.09.019
Vo DBagheri EKompatsiaris IPapadopoulos STsikrika TVrochidis S(2015)Syntactic and semantic structures for relation extractionProceedings of the 6th Symposium on Future Directions in Information Access10.14236/ewic/FDIA2015.7(28-33)Online publication date: 2-Sep-2015
https://dl.acm.org/doi/10.14236/ewic/FDIA2015.7
Luo CGuan RWang ZLin C(2014)HetPathMineProceedings of the 36th European Conference on IR Research on Advances in Information Retrieval - Volume 841610.5555/2964060.2964170(210-221)Online publication date: 13-Apr-2014
https://dl.acm.org/doi/10.5555/2964060.2964170
Mu TMiwa MTsujii JAnaniadou S(2014)DISCOVERING ROBUST EMBEDDINGS IN DISSIMILARITY SPACE FOR HIGH-DIMENSIONAL LINGUISTIC FEATURESComputational Intelligence10.1111/j.1467-8640.2012.00452.x30:2(285-315)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1111/j.1467-8640.2012.00452.x
Trindade LWang HBlackburn WTaylor P(2014)Enhanced Factored Sequence Kernel for Sentiment ClassificationProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0210.1109/WI-IAT.2014.142(519-525)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1109/WI-IAT.2014.142
Raman KSwaminathan AGehrke JJoachims TGrossman RUthurusamy RDhillon IKoren Y(2013)Beyond myopic inference in big data pipelinesProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2487588(86-94)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2487575.2487588
Tu ZHe YFoster Jvan Genabith JLiu QLin SLi HLin COsborne M(2012)Identifying high-impact sub-structures for convolution kernels in document-level sentiment classificationProceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 210.5555/2390665.2390742(338-343)Online publication date: 8-Jul-2012
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Patwardhan SBoguraev BAgarwal AMoschitti AChu-Carroll JChen XLebanon GWang HZaki M(2012)Labeling by landscapingProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398412(1133-1142)Online publication date: 29-Oct-2012
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