Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks

Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which require many complex, hand-designed features. In addition, the words encoded by one-hot are unable to represent semantic information. Therefore, we utilize dependency-based embeddings to represent words semantically and syntactically. Then, we propose a parallel multi-pooling convolutional neural network (PMCNN) model to capture the compositional semantic features of sentences. Furthermore, we employ a rectified linear unit, which creates sparse representations with true zeros, and which is adapted to the biomedical event extraction, as a nonlinear function in PMCNN architecture. The experimental results from MLEE dataset show that our approach achieves an $F1$ score of 80.27 percent in trigger identification and an $F1$ score of 59.65 percent in biomedical event extraction, which performs better than other state-of-the-art methods.

1 Introduction

Avast and ever-expanding body of natural language text is becoming increasingly difficult to leverage. This is particularly true in the domain of biomedical research articles, which are increasing exponentially in number. Consequently, the need to extract interested and structured information automatically from biomedical text continues to grow. Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction.