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Optimization of hierarchical reinforcement learning relationship extraction model

Qihang Wu (Sun Yat-sen University, Guangzhou, China)
Daifeng Li (School of Information Management, Sun Yat-sen University, Guangzhou, China)
Lu Huang (Sun Yat-sen University, Guangzhou, China)
Biyun Ye (Sun Yat-sen University, Guangzhou, China)

Information Discovery and Delivery

ISSN: 2398-6247

Article publication date: 1 May 2020

Issue publication date: 13 August 2020

172

Abstract

Purpose

Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.

Design/methodology/approach

This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.

Findings

Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.

Originality/value

The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.

Keywords

Acknowledgements

This research is supported by Chinese National Youth Foundation Research (Grant No: 61702564), Soft Science Foundation of Guangdong Province (Grant No: 2019A101002020), Talent Scientific Research Foundation of Sun Yat-sen University (Grant No: 20000-18841202).

Citation

Wu, Q., Li, D., Huang, L. and Ye, B. (2020), "Optimization of hierarchical reinforcement learning relationship extraction model", Information Discovery and Delivery, Vol. 48 No. 3, pp. 129-136. https://doi.org/10.1108/IDD-01-2020-0005

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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