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.

本文提出一种利用递归神经网络和词语和实体类型嵌入作为特征提取高层特征的方法，避免使用各种自然语言处理工具生成的复杂手工特征。我们的实验显示在多层事件抽取 (MLEE) 语料库上达到了最先进的 F1 得分，并对结果进行了类别分析和讨论了在触发器识别任务中各种特征的重要性。