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Employing Constituent Dependency Information for Tree Kernel-Based Semantic Relation Extraction between Named Entities

Authors:

Qiaoming ZhuAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 10, Issue 3

Article No.: 15, Pages 1 - 24

https://doi.org/10.1145/2002980.2002985

Published: 01 September 2011 Publication History

Abstract

This article proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction between named entities. The basic idea is to employ constituent dependency information in keeping the necessary nodes and their head children along the path connecting the two entities in the syntactic parse tree, while removing the noisy information from the tree, eventually leading to a dynamic syntactic parse tree. This article also explores various entity features and their possible combinations via a unified syntactic and semantic tree framework, which integrates both structural syntactic parse information and entity-related semantic information. Evaluation on the ACE RDC 2004 English and 2005 Chinese benchmark corpora shows that our dynamic syntactic parse tree much outperforms all previous tree spans, indicating its effectiveness in well representing the structural nature of relation instances while removing redundant information. Moreover, the unified parse and semantic tree significantly outperforms the single syntactic parse tree, largely due to the remarkable contributions from entity-related semantic features such as its type, subtype, mention-level as well as their bi-gram combinations. Finally, the best performance so far in semantic relation extraction is achieved via a composite kernel, which combines this tree kernel with a linear, state-of-the-art, feature-based kernel.

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Cited By

Liu XZhang HCheng Y(2024)Feature Extraction of Dialogue Text Based on Big Data and Machine LearningInternational Journal of Web-Based Learning and Teaching Technologies10.4018/IJWLTT.33760219:1(1-15)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.4018/IJWLTT.337602
Xiao S(2023)The Application of Micro-Video Technology in the Promotion of Digital Reading in Children's Libraries Under the Background of Media FusionInternational Journal of Web-Based Learning and Teaching Technologies10.4018/IJWLTT.32051818:2(1-15)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.4018/IJWLTT.320518
Warto Muljono Purwanto Noersasongko E(2022)Capitalization Feature and Learning Rate for Improving NER Based on RNN BiLSTM-CRF2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)10.1109/CyberneticsCom55287.2022.9865660(398-403)Online publication date: 16-Jun-2022
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Index Terms

Employing Constituent Dependency Information for Tree Kernel-Based Semantic Relation Extraction between Named Entities
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image ACM Transactions on Asian Language Information Processing

ACM Transactions on Asian Language Information Processing Volume 10, Issue 3

September 2011

114 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/2002980

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 September 2011

Accepted: 01 April 2011

Revised: 01 February 2011

Received: 01 November 2010

Published in TALIP Volume 10, Issue 3

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Cited By

Liu XZhang HCheng Y(2024)Feature Extraction of Dialogue Text Based on Big Data and Machine LearningInternational Journal of Web-Based Learning and Teaching Technologies10.4018/IJWLTT.33760219:1(1-15)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.4018/IJWLTT.337602
Xiao S(2023)The Application of Micro-Video Technology in the Promotion of Digital Reading in Children's Libraries Under the Background of Media FusionInternational Journal of Web-Based Learning and Teaching Technologies10.4018/IJWLTT.32051818:2(1-15)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.4018/IJWLTT.320518
Warto Muljono Purwanto Noersasongko E(2022)Capitalization Feature and Learning Rate for Improving NER Based on RNN BiLSTM-CRF2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)10.1109/CyberneticsCom55287.2022.9865660(398-403)Online publication date: 16-Jun-2022
https://doi.org/10.1109/CyberneticsCom55287.2022.9865660
Sun YLu CBie RZhang J(2016)Semantic relation computing theory and its applicationJournal of Network and Computer Applications10.1016/j.jnca.2014.09.01759:C(219-229)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.jnca.2014.09.017
Zheng SYang JLin XGu J(2012)Dependency Tree Based Chinese Relation Extraction over Web DataProceedings of the 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems10.1109/KICSS.2012.32(104-110)Online publication date: 8-Nov-2012
https://dl.acm.org/doi/10.1109/KICSS.2012.32
Liu DZhao ZHu YQian L(2012)Incorporating lexical semantic similarity to tree kernel-based chinese relation extractionProceedings of the 13th Chinese conference on Chinese Lexical Semantics10.1007/978-3-642-36337-5_2(11-21)Online publication date: 6-Jul-2012
https://dl.acm.org/doi/10.1007/978-3-642-36337-5_2

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