Abstract
Biomedical event extraction is a challenging task in biomedical text mining, which plays an important role in improving biomedical research and disease prevention. As the crucial and prerequisite step in event extraction, biomedical trigger detection has attracted much attention. Previous approaches usually depended on feature engineering with unbalanced data. In this paper, we propose a two-stage method based on hybrid neural network for trigger detection, which divides trigger detection into recognition stage and classification stage. In the first stage, we build a BiLSTM based recognition model integrating attention mechanism (Att-BiLSTM). In the second stage, the classification model based on Passive-Aggressive online algorithm is constructed. Furthermore, to enrich sentence-level features, we establish sentence embeddings and add reading gate. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 80.26%, which achieves the state-of-the-art systems.
Highlights
With the development of Internet, the resources are expanding at an exponential speed, especially in the biomedical domain, which has made it harder than ever for scientists to research and extract knowledge from the vast unstructured biomedical scientific literature
To further improve the performance, we propose a two-stage method, which divides trigger detection into recognition stage and classification stage
Figure 2 is our framework of trigger detection, which consists of two parts: the input representation of the data, and the trigger detection model based on hybrid neural network integrating two-stage method
Summary
With the development of Internet, the resources are expanding at an exponential speed, especially in the biomedical domain, which has made it harder than ever for scientists to research and extract knowledge from the vast unstructured biomedical scientific literature. Biomedical information extraction techniques arise and develop rapidly. Biomedical information extraction mainly studies how to extract useful information from a large number of biomedical literatures automatically. Event extraction, which is an effective way to represent the structured knowledge from unstructured text, is a fundamental technology for information extraction. As the prerequisite step of biomedical event extraction, biomedical event trigger identification has received extensive attention. Biomedical event extraction can help biomedical scientists to do research conveniently, and provide inspiration
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