Abstract
Event trigger detection, which plays a key role in biomedical event extraction, has attracted significant attention recently. However, most approaches are based on statistical models, much relying on complex hand-designed features. In this paper, we utilise the ability of Convolutional Neural Network CNN for addressing higher-level features automatically to explore correlations between a trigger and an event type. We only keep one candidate trigger along with N-words around it and entity mention features as a raw input, giving up complex input with hand-designed features that derived from currently existed Natural Language Processing NLP tools. Our experiments on Multi-Level Event Extraction MLEE corpus showed that the method achieved a higher F-score of 78.67% compared to the state-of-the-art approaches. The results demonstrate that the proposed method is effective for event trigger detection.
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