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

Authors:
Roxana Girju;Ana-Maria Giuglea;Marian Olteanu;Ovidiu Fortu;Orest Bolohan;Dan Moldovan
Affiliations:
Baylor University, Waco, Texas;University of Texas at Dallas, Dallas, Texas;University of Texas at Dallas, Dallas, Texas;University of Texas at Dallas, Dallas, Texas;University of Texas at Dallas, Dallas, Texas;University of Texas at Dallas, Dallas, Texas
Venue:
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Year:
2004

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Abstract

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.