Support Vector Machines Applied To The Classification Of Semantic Relations In Nominalized Noun Phrases

Paper: Support Vector Machines Applied To The Classification Of Semantic Relations In Nominalized Noun Phrases

ACL ID	W04-2610
Title	Support Vector Machines Applied To The Classification Of Semantic Relations In Nominalized Noun Phrases
Venue	Computational Lexical Semantics Workshop
Session
Year	2004
Authors	Roxana Gîrju (Baylor University, Waco TX) Ana-Maria Giuglea (University of Texas at Dallas, Richardson TX) Marian Olteanu Ovidiu Fortu Orest Bolohan Dan Moldovan

The discovery of semantic relations in text plays an important role in many NLP appli- cations. 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 fea- tures are extracted automatically and used in a Support Vector Machine learning model. The paper presents preliminary results for the se- mantic classification of the most representative NP patterns using four distinct learning mod- els.

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