Open Access

ARTICLE

Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

Zhaojie Sun¹, Linlin Xing^1,*, Longbo Zhang¹, Hongzhen Cai², Maozu Guo³

1 Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
2 Department of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, 255000, China
3 Department of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

* Corresponding Author: Linlin Xing. Email:

Computers, Materials & Continua 2024, 80(3), 4325-4340. https://doi.org/10.32604/cmc.2024.053588

Received 05 May 2024; Accepted 11 August 2024; Issue published 12 September 2024

Abstract

Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we regard joint extraction as a tripartite classification problem. At the same time, from the perspective of triple classification, we design a multi-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs. Finally, we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction. Our model (MCTPL) Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches. Finally, we evaluated our model on two publicly accessible datasets. The experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34% compared to the current optimal model. On the DDI dataset, the F1 value improves the F1 value by 1.68% compared to the current optimal model. Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

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Keywords

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