The Choice of Features for Classification of Verbs in Biomedical Texts

We conduct large-scale experiments to investigate optimal features for classification of verbs in biomedical texts. We introduce a range of feature sets and associated extraction techniques, and evaluate them thoroughly using a robust method new to the task: cost-based framework for pairwise clustering. Our best results compare favourably with earlier ones. Interestingly, they are obtained with sophisticated feature sets which include lexical and semantic information about selectional preferences of verbs. The latter are acquired automatically from corpus data using a fully unsupervised method.

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