DrugDrug Interaction Extraction Using a Spanbased Neural Network Model Drugdrug interaction (DDI) extraction is one of important tasks in biomedical text mining. Compared with traditional pipeline extraction, joint methods enjoy lower error propagation, achieving better performances. However, biomedical texts contain a large number of overlapped relations, and existing joint extraction models fail to capture these entity relations. In this paper, we propose a novel joint model for DDI extraction. Different from the existing sequential models, our spanbased neural model treats the task as a relation triplets prediction problem, and builds the entitygraph by enumerating all possible candidate spans, and simultaneously models the relation between the correlated entities. Experimental results show that the proposed method significantly outperforms the strong baselines and previous methods, achieving the stateoftheart performances.

Extracting Drugdrug Interactions with a Dependencybased Graph Convolution Neural Network Drugdrug interactions (DDIs) play a key role in various applications of biomedicine such as pharmacovigilance. DDIs are frequently reported in biomedical publications, making them an effective source for DDI extraction. Although neural networks have achieved competitive performances in DDI extraction, previous work depended on dependency paths to remove noise from the sentences of biomedical publications. However, such method may ignore crucial information about DDIs. Effectively exploiting much dependency information can improve DDI extraction. In this article, we proposed a model that combines the graph convolution neural network (GCNN) and bidirectional long shortterm memory (BiLSTM) to extract DDI interactions from entire dependency graphs of sentences. We evaluated our model in the benchmark corpus for this domain, namely the DDIExtraction 2013 corpus. Our model achieved the stateoftheart result (77.0% in F1), which are superior to the results reported in previous work. The code is available at https:github.comwoodyXwtDDI_extraction.