Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2016

Abstract

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on two widely used datasets: SemEval-2010 Task 8 and ACE-2005. The empirical results show that our combined model together with mirror instances achieves the state-of-the-art results on both datasets.

Keywords

Computational linguistics, Mirrors, Neural networks, Semantics, Text processing, Combined model, Convolutional neural network, Relation classifications, Semantic relations, State of the art, Classification (of information)

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of COLING 2016: The 26th International Conference on Computational Linguistics: Osaka, Japan, December 11-17

First Page

2373

Last Page

2377

ISBN

9784879747020

Publisher

Association for Computational Linguistics

City or Country

Stroudsburg, PA

Copyright Owner and License

Authors

Additional URL

https://aclweb.org/anthology/P15-1037

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