Barzegar, Siamak and Freitas, Andre and Handschuh, Siegfried and Davis, Brian (2017) Composite Semantic Relation Classification. In: Natural Language Processing and Information Systems. Lecture Notes in Computer Science (10260). Springer, Cham, Switzerland, pp. 406-417. ISBN 978-3-319-59568-9

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Abstract

Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.

Item Type:	Book Section
Additional Information:	About the Conference: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21–23, 2017
Keywords:	Semantic relation; Distributional semantic; Deep learning; Classification;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	11828
Identification Number:	https://doi.org/10.1007/978-3-319-59569-6
Depositing User:	IR Editor
Date Deposited:	28 Nov 2019 10:12
Publisher:	Springer
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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