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Improved Distant Supervision Relation Extraction Based on Edge- Reasoning Hybrid Graph Model

15 Pages Posted: 19 Oct 2021 Publication Status: Accepted

See all articles by Shirong Shen

Shirong Shen

Southeast University

Shangfu Duan

Southeast University

Huan Gao

Southeast University

Guilin Qi

Southeast University - School of Computer Science and Engineering

Abstract

Distant supervision relation extraction (DSRE) trains a classifier by automatically labeling data through aligning triples in the knowledge base (KB) with large-scale corpora. Training data generated by distant supervision may contain many mislabeled instances, which is harmful to the training of the classifier. Some recent methods show that relevant background information in KBs, such as entity type (e.g., Organization and Book), can improve the performance of DSRE. However, there are three main problems with these methods. Firstly, these methods are tailored for a specific type of information. A specific type of information only has a positive effect on a part of instances and will not be beneficial to all cases. Secondly, different background information is embedded independently, and no reasonable interaction is achieved. Thirdly, previous methods do not consider the side effect of the introduced noise of background information. To address these issues, we leverage five types of background information instead of a specific type of information in previous works and propose a novel edge-reasoning hybrid graph (ER-HG) model to realize reasonable interaction between different kinds of information. In addition, we further employ an attention mechanism for the ER-HG model to alleviate the side effect of noise. The ER-HG model integrates all types of information efficiently and is very robust to the noise of information. We conduct experiments on two widely used datasets. The experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in held-out metric and robustness tests.

Suggested Citation

Shen, Shirong and Duan, Shangfu and Gao, Huan and Qi, Guilin, Improved Distant Supervision Relation Extraction Based on Edge- Reasoning Hybrid Graph Model. Journal of Web Semantics First Look , Available at SSRN: https://ssrn.com/abstract=3945436 or http://dx.doi.org/10.2139/ssrn.3945436

Shirong Shen (Contact Author)

Southeast University ( email )

Sipailou 2#
Nanjing, Jiangsu Province 210096
China

Shangfu Duan

Southeast University ( email )

Sipailou 2#
Nanjing, Jiangsu Province 210096
China

Huan Gao

Southeast University ( email )

Sipailou 2#
Nanjing, Jiangsu Province 210096
China

Guilin Qi

Southeast University - School of Computer Science and Engineering ( email )

Sipailou 2#
Nanjing, Jiangsu Province 210096
China

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