关键词: 生物医学文献, 关系提取, 注意力机制, 多通道

Abstract:

Biomedical researchers often search the literature for interactions between biological entities, such as drug-drug interactions, chemical-protein interactions. With the rapid growth of biomedical literature and the development of deep learning, automatic extraction of biological entity interactions from literature has shown great potential. The previous methods using deep learning have achieved certain results, but there are some problems as follows：The static word vector is used in the model, which can’t distinguish the polysemy of a word; the weight of the word is not considered, and the effect of long sentence extraction is poor; it improves the sample imbalance problem by ensembles of models, which is more complex. Therefore, the paper proposes a deep Multi-Channel CNN（MCCNN） model based on residual structure, which uses BERT（Bidirectional Encoder Representation from Transformers） to generate dynamic word vectors to improve the accuracy of word semantic representation, and uses multi-head attention to capture long sentence dependencies, and reduces the impact of sample imbalance through the Ranking loss function instead of ensembles of models. Experiments on several data sets show that the proposed method is effective.

Key words: biomedical literature, relation extraction, attention mechanisms, multi-channel