Automatic Acquisition of Grammatical Types for Nouns

The work1 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.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Núria Bel, Sergio Espeja, Montserrat Marimon
• Keywords: Computational Linguistics, Deep Grammatical Information, Learner, NAACL 2007, Nouns