A Two-Stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings

Extracting biomedical events from biomedical literature plays an important role in the field of biomedical text mining, and the trigger detection is a key step in biomedical event extraction. We propose a two-stage method for trigger detection, which divides trigger detection into recognition stage and classification stage, and different features are selected in each stage. In the first stage, we select the features which are more suitable for recognition, and in the second stage, the features that are more helpful to classification are adopted. Furthermore, we integrate word embeddings to represent words semantically and syntactically. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 79.75 percent, which outperforms the state-of-the-art systems.

1 Introduction

With the rapid spread of Internet, the scientific biomedical literature is expanding at an exponential speed, which has made it harder than ever for scientists to research, manage, and extract knowledge from unstructured text in their research field. To tackle these problems, nature language processing (NLP) and text mining(TM) techniques are rapidly developing.