Star-BiLSTM-LAN for Document-level Mutation-Disease Relation Extraction from Biomedical Literature

Relations between mutations and diseases hiding in biomedical literature are valuable for the analysis and interpretation of many complex diseases, which can help explore more effective treatment options for corresponding diseases. Most current document-level mutation-disease relation extraction methods are based on classification approaches and suffer the lack of ability to extract inter-sentential relations. To solve this problem, we regard extracting document-level mutation-disease relations as a sequence tagging task and propose a neural network-based method called Star-BiLSTM-LAN. By combining the star transformer and the Bi-directional Long Short-Term Memory network, this method achieves a strong ability to capture semantic and syntactic information at the document level from different aspects, and it can discover internal representations that prove useful for the task of interest. Star-BiLSTM-LAN is evaluated on EMU BCa and PCa datasets, and achieves the state-of-the-art F-scores of 89.20% and 90.43%, which are 4.70% and 2.43% higher than the baseline, respectively. Also, the proposed method achieves an F-score of 94.40% on BRONCO dataset.