FBK NK: a WordNet-based System for Multi-Way Classification of Semantic Relations
Matteo Negri, Milen Kouylekov · 2013
We describe a WordNet-based system for the extraction of semantic relations between pairs of nominals appearing in English texts. The system adopts a lightweight approach, based on training a Bayesian Network classifier using large sets of binary features. Our features consider: i) the context surrounding the annotated nominals, and ii) different types of knowledge extracted from WordNet, including direct and explicit relations between the annotated nominals, and more general and implicit evidence (e.g. semantic boundary collocations). The system achieved a Macro-averaged F1 of 68.02% on the “Multi-Way Classification of Semantic