Knowledge Guided Attention and Graph Convolutional Networks for Chemical-Disease Relation Extraction

The automatic extraction of the chemical-disease relation (CDR) from the text becomes critical because it takes a lot of time and effort to extract valuable CDR manually. Studies have shown that prior knowledge from the biomedical knowledge base is important for relation extraction. The method of co...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 20; číslo 1; s. 489 - 499
Hlavní autoři: Sun, Yi, Wang, Jian, Lin, Hongfei, Zhang, Yijia, Yang, Zhihao
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Attention
Biological system modeling
Chemicals
Connectionism and neural nets
Data mining
Deep learning
Diseases
Embedding
Feature extraction
Knowledge
Knowledge and data engineering tools and techniques
Knowledge based systems
Knowledge Bases
Knowledge bases (artificial intelligence)
Language
Syntactics
Text analysis
ISSN:1545-5963, 1557-9964, 1557-9964
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Shrnutí:The automatic extraction of the chemical-disease relation (CDR) from the text becomes critical because it takes a lot of time and effort to extract valuable CDR manually. Studies have shown that prior knowledge from the biomedical knowledge base is important for relation extraction. The method of combining deep learning models with prior knowledge is worthy of our study. In this paper, we propose a new model called Knowledge Guided Attention and Graph Convolutional Networks (KGAGN) for CDR extraction. First, to make full advantage of domain knowledge, we train entity embedding as a feature representation of input sequence, and relation embedding to capture weighted contextual information further through the attention mechanism. Then, to make full advantage of syntactic dependency information in cross-sentence CDR extraction, we construct document-level syntactic dependency graphs and encode them using a graph convolution network (GCN). Finally, the chemical-induced disease (CID) relation is extracted by using weighted context features and long-range dependency features both of which contain additional knowledge information We evaluated our model on the CDR dataset published by the BioCreative-V community and achieves an F1-score of 73.3%, surpassing other state-of-the-art methods. the code implemented by PyTorch 1.7.0 deep learning library can be downloaded from Github: https://github.com/sunyi123/cdr .
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2021.3135844