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The discovery of semantic relations in text plays an important role in many NLP applications. This paper presents a method for the automatic classification of semantic relations in nominalized noun phrases. Nominalizations represent a subclass of NP constructions in which either the head or the modifier noun is derived from a verb while the other noun is an argument of this verb. Especially designed features are extracted automatically and used in a Support Vector Machine learning model. The paper presents preliminary results for the semantic classification of the most representative NP patterns using four distinct learning models.

Support vector machines applied to the classification of semantic relations in nominalized noun phrases