A hierarchical convolutional model for biomedical relation extraction

Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. In biomedical literature, the sentence lengths are longer compared to general domain. Because all entity pairs in a sentence share the same contextual features and each of them should be evaluated, the biomedical relation extract task suffers from more serious semantic overlapping and data imbalance problems. In this study, a hierarchical convolutional model is proposed to address these problems. This model organizes the convolution operation in a hierarchical structure. First, after a sentence is divided into several channels by using the semantic structure of a relation instance, a token-level convolution is used to encodes local contextual features of different channels. Then, a channel-level convolution is designed to encode global semantic dependencies of a sentence. The hierarchical convolution is effective to learn contextual features and semantic dependencies relevant to ordered named entities, which enhance the discriminability of a neural network for biomedical relation extraction. The proposed method was evaluated on seven public datasets for three tasks: including protein-protein, drug-drug, and chemical-protein interactions. It outperformed state-of-the-art performance about 3.7% in the F1 score.

Automated summary

Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.