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
Citation
KUANG, Jun; CAO, Yixin; ZHENG, Jianbing; HE, Xiangnan; GAO, Ming; and ZHOU, Aoying.
Improving neural relation extraction with implicit mutual relations. (2020). Proceedings of the 36th IEEE International Conference on Data Engineering, Dallas, Texas, 2020 April 20-24. 1021-1032.
Available at: https://ink.library.smu.edu.sg/sis_research/7480
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDE48307.2020.00093