A Concise Relation Extraction Method Based on the Fusion of Sequential and Structural Features Using ERNIE

Yu Wang, Yuan Wang, Zhenwan Peng, Feifan Zhang and Fei Yang ()
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Yu Wang: School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China
Yuan Wang: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230001, China
Zhenwan Peng: School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China
Feifan Zhang: School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China
Fei Yang: School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China

Mathematics, 2023, vol. 11, issue 6, 1-20

Abstract: Relation extraction, a fundamental task in natural language processing, aims to extract entity triples from unstructured data. These triples can then be used to build a knowledge graph. Recently, pre-training models that have learned prior semantic and syntactic knowledge, such as BERT and ERNIE, have enhanced the performance of relation extraction tasks. However, previous research has mainly focused on sequential or structural data alone, such as the shortest dependency path, ignoring the fact that fusing sequential and structural features may improve the classification performance. This study proposes a concise approach using the fused features for the relation extraction task. Firstly, for the sequential data, we verify in detail which of the generated representations can effectively improve the performance. Secondly, inspired by the pre-training task of next-sentence prediction, we propose a concise relation extraction approach based on the fusion of sequential and structural features using the pre-training model ERNIE. The experiments were conducted on the SemEval 2010 Task 8 dataset and the results show that the proposed method can improve the F1 value to 0.902.

Keywords: relation extraction; pre-training models; BERT; ERNIE; shortest dependency path; fusion methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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