Title: Selection gate-based networks for semantic relation extraction

Authors: Jun Sun; Yan Li; Yatian Shen; Lei Zhang; Wenke Ding; Xianjin Shi; Xiajiong Shen; Guilin Qi; Jing He

Addresses: School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475000, China ' School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475000, China ' School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210000, China ' School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475000, China ' School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475000, China ' School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475000, China ' School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475000, China ' School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210000, China ' The Corporate and Investment, Bank Technology, J.P. Morgan Chase N.A., 25 Bank St., Canary Wharf, London, E145JP, UK

Abstract: Semantic relatedness between context information and entities, which is one of the most easily accessible features, has been proven to be very useful for detecting the semantic relation held in the text segment. However, some methods fail to take into account important information between entities and contexts. How to effectively choose the closest and the most relevant information to the entity in context words in a sentence is an important task. In this paper, we propose selection gate-based networks (SGate-NN) to model the relatedness of an entity word with its context words, and select the relevant parts of contexts to infer the semantic relation toward the entity. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrate that the proposed method is effective for relation classification, which can obtain state-of-the-art classification accuracy.

Keywords: relation extraction; selection gate networks; neural networks.

DOI: 10.1504/IJES.2021.116100

International Journal of Embedded Systems, 2021 Vol.14 No.3, pp.211 - 217

Received: 11 May 2019
Accepted: 07 Aug 2019
Published online: 12 Jul 2021 *