ILK: machine learning of semantic relations with shallow features and almost no data

Authors:
Iris Hendrickx;Roser Morante;Caroline Sporleder;Antal van den Bosch
Affiliations:
Uversity of Antwerp, Wilrijk, Belgium;Tilburg University, Tilburg, The Netherlands;Tilburg University, Tilburg, The Netherlands;Tilburg University, Tilburg, The Netherlands
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

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Abstract

This paper summarizes our approach to the Semeval 2007 shared task on "Classification of Semantic Relations between Nominals". Our overall strategy is to develop machine-learning classifiers making use of a few easily computable and effective features, selected independently for each classifier in wrapper experiments. We train two types of classifiers for each of the seven relations: with and without WordNet information.