Comunicació presentada a: Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics 2007, celebrada a New York, United States of America, del 22 al 27 d'abril de 2007.

The work we present here is concerned with the acquisition of deep grammatical information for nouns in Spanish. The aim is to build a learner that can handle noise, but, more interestingly, that is able to overcome the problem of sparse data, especially important in the case of nouns. We have based our work on two main points. Firstly, we have used distributional evidences as features. Secondly, we made the learner deal with all occurrences of a word as a single complex unit. The obtained results show that grammatical features of nouns is a level of generalization that can be successfully approached with a Decision Tree learner.

This research was supported by the Spanish Ministerio de Educación y Ciencia: project AAILE, HUM2004-05111-C02-01/FILO, Ramón y Cajal, Juan de la Cierva Programs and PTA-CTE/1370/2003 with Fondo Social Europeo.