Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2020

Abstract

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity graph based on the implicit mutual relations, we preserve the semantic relations of entity pairs via embedding each vertex of the graph into a low-dimensional space. As a result, we can easily and flexibly integrate the implicit mutual relations and other entity information, such as entity types, into the existing RE methods.Our experimental results on a New York Times and another Google Distant Supervision datasets suggest that our proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant.

Keywords

Relation extraction, implicit mutual relations, unlabeled data, entity information

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 36th IEEE International Conference on Data Engineering, Dallas, Texas, 2020 April 20-24

First Page

1021

Last Page

1032

Identifier

10.1109/ICDE48307.2020.00093

Publisher

IEEE

City or Country

Dallas, Texas

Additional URL

https://doi.org/10.1109/ICDE48307.2020.00093

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