TCS Research at TAC 2017: Joint Extraction of Entities and Relations from Drug Labels using an Ensemble of Neural Networks

We describe our submission at TAC 2017 for extracting entities and relations of interest from drug labels. We employ an end-to-end relation extraction system which jointly extracts both entities and relations. The task of end-to-end relation extraction consists of identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Based on our earlier work (Pawar et al., 2017), a single neural network model (“All Word Pairs” model i.e. AWP-NN) is trained to assign an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. Moreover, we build an ensemble of multiple AWPNN models to achieve better performance than the individual models. We achieved 73.18% and 24.79% F-measures for entity and relation extraction, respectively.