Barzegar, Siamak and Davis, Brian and Handschuh, Siegfried and Freitas, Andre
(2018)
Classification of composite semantic relations by a distributional-relational model.
Data & Knowledge Engineering, 117.
pp. 319-335.
ISSN 0169-023X
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 semantic relation classification task.
The proposed model is different from existing approaches which typically use machine learning
models built over lexical and distributional word vector features in that is uses a 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.
| Item Type: |
Article
|
| Keywords: |
Semantic relation;
Distributional semantic;
Deep learning;
Classification; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
13241 |
| Identification Number: |
https://doi.org/10.1016/j.datak.2018.06.005 |
| Depositing User: |
IR Editor
|
| Date Deposited: |
17 Sep 2020 11:05 |
| Journal or Publication Title: |
Data & Knowledge Engineering |
| Publisher: |
Elsevier |
| Refereed: |
Yes |
| URI: |
|
| 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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