Fast recursive multi-class classification of pairs of text entities for biomedical event extraction
Abstract
Extracting biomedical events from scientific articles to automatically update dedicated knowledge bases has become a popular research topic with important applications. Most existing approaches are either pipeline models of specific classifiers, usually subject to cascading errors, or joint structured models, more efficient but also more costly and complicated to train. This paper proposes a system based on a pairwise model that transforms event extraction into a simple multi-class problem of classifying pairs of text entities. Such pairs are recursively provided to the classifier, allowing to extract events involving other events as arguments. This model facilitates inference compared to joint models while % relying on a single main classifier compared to being more direct and efficient than usual pipeline approaches. This method yields the best results reported so far on the BioNLP 2011 and 2013 Genia tasks.
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