TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Protein-Protein Interaction Extraction Approach Based on Large Pre-trained Language Model and Adversarial Training |
| 영문제목(English Title) |
A Protein-Protein Interaction Extraction Approach Based on Large Pre-trained Language Model and Adversarial Training |
| 저자(Author) |
Zhan Tang
Xuchao Guo
Zhao Bai
Lei Diao
Shuhan Lu
Lin Li
|
| 원문수록처(Citation) |
VOL 16 NO. 03 PP. 0771 ~ 0791 (2022. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL. |
| 키워드(Keyword) |
adversarial training
information extraction
natural language processing
pretrained language model
protein-protein interaction
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| 파일첨부 |
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