Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
This addresses the problem of automating biomedical event extraction for researchers, though it is incremental as it builds on existing RNN methods for a specific bottleneck.
The paper tackled biomedical event trigger identification by proposing a bidirectional recurrent neural network (RNN) model to extract higher-level features across sentences, achieving state-of-the-art F1-score on the Multi Level Event Extraction (MLEE) corpus.
Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.