Dependency-based long short term memory network for drug-drug interaction extraction

• Main Authors: Wei Wang, Xi Yang, Canqun Yang, Xiaowei Guo, Xiang Zhang, Chengkun Wu
• Format: Article
• Language: English
• Published: BMC 2017-12-01
• Series: BMC Bioinformatics
• Subjects: Relation extraction; Long short term memory; Dependency tree; Data imbalance
• Online Access: http://link.springer.com/article/10.1186/s12859-017-1962-8

Abstract Background Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification. Methods We propose a dependency-based deep neural network model for DDI extraction. By introducing the dependency-based technique to a bi-directional long short term memory network (Bi-LSTM), we build three channels, namely, Linear channel, DFS channel and BFS channel. All of these channels are constructed with three network layers, including embedding layer, LSTM layer and max pooling layer from bottom up. In the embedding layer, we extract two types of features, one is distance-based feature and another is dependency-based feature. In the LSTM layer, a Bi-LSTM is instituted in each channel to better capture relation information. Then max pooling is used to get optimal features from the entire encoding sequential data. At last, we concatenate the outputs of all channels and then link it to the softmax layer for relation identification. Results To the best of our knowledge, our model achieves new state-of-the-art performance with the F-score of 72.0% on the DDIExtraction 2013 corpus. Moreover, our approach obtains much higher Recall value compared to the existing methods. Conclusions The dependency-based Bi-LSTM model can learn effective relation information with less feature engineering in the task of DDI extraction. Besides, the experimental results show that our model excels at balancing the Precision and Recall values.