Classification of Composite Semantic Relations by a Distributional-Relational Model
Journal
Data & Knowledge Engineering
Type
journal article
Date Issued
2018-09
Author(s)
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 using one or more relations between entities/term mentions, extending the traditional seman- tic 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. The proposed approach outperformed existing baselines with regard to F1-score, Accuracy, Precision and Recall.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Elsevier
Publisher place
St.Gallen
Volume
117
Start page
319
End page
335
Subject(s)
Division(s)
Contact Email Address
siegfried.handschuh@unisg.ch
Eprints ID
258181
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classification-composite-semantic.pdf
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Format
Adobe PDF
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