Bio-molecular event extraction by integrating multiple event-extraction systems

Abstract

Event extraction from biomedical text is a very important task in text mining and natural language processing. The overall task involves finding event-related expressions, classifying these into predefined categories and attaching arguments to these events. We perform event detection and event classification in one step using an ensemble of classifiers. For event argument extraction, we also use an ensemble of classification models. Our base models are developed using supervised machine learning that makes use of statistical, contextual and syntactic features. Our experimental result on the benchmark datasets of BioNLP-2011 shared task shows the recall, precision and F-measure values of 51.20%, 65.78% and 57.58%, respectively.