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A hybrid model based on neural networks for biomedical relation extraction.
Yijia Zhang1, Hongfei Lin1, Zhihao Yang1
1College of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China.
Journal of Biomedical Informatics
|March 31, 2018
Summary
This study introduces a hybrid model combining Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) for improved biomedical relation extraction. This approach effectively boosts the performance of mining biomedical knowledge from text.
Area of Science:
- Biomedical informatics
- Natural Language Processing
- Computational Biology
Background:
- Biomedical relation extraction is crucial for uncovering hidden knowledge in scientific literature.
- Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) are primary methods for this task, each with distinct strengths.
- Current methods often utilize RNNs or CNNs independently, potentially missing synergistic benefits.
Purpose of the Study:
- To develop and evaluate a hybrid model integrating RNNs and CNNs for enhanced biomedical relation extraction.
- To leverage the complementary advantages of both RNNs and CNNs in feature learning for relation extraction.
- To improve the accuracy and efficiency of extracting biomedical knowledge from text.
Main Methods:
- A hybrid model combining RNNs and CNNs was developed for biomedical relation extraction.
- The shortest dependency path (SDP) was extracted and segmented into word and relation sequences.
- RNNs processed sentence sequences, while CNNs processed dependency sequences; their outputs were combined for relation detection.
Main Results:
- The hybrid RNN-CNN model demonstrated improved performance in biomedical relation extraction tasks.
- Experiments on protein-protein interaction (PPI) and drug-drug interaction (DDI) corpora validated the model's effectiveness.
- The results confirm that the strengths of RNNs and CNNs are complementary in this domain.
Conclusions:
- Combining RNNs and CNNs offers a synergistic approach to biomedical relation extraction.
- The proposed hybrid model effectively enhances the mining of biomedical knowledge.
- This method shows significant potential for advancing automated biomedical knowledge discovery.
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