Stress assignment in Spanish proper names

In this paper, we propose an approach for Stress Assignment in Spanish Proper Names, based on a Multi-Layer Perceptron (MLP). When assigning stress to a word, we first analyse each vowel in the word and then calculate a Stress-Confidence Measure for it, using a MLP. The system will assign the stress to the vowel with the highest stress-confidence measure. In this paper we present and analyse different alternatives for the inputs to the Multi-Layer Perceptron. In all cases, we consider the number of vowels in the name and the vowel position in the word (taking into account only the vowels in the analysed word). For the rest of inputs, we consider a window of letters. These letters are obtained from the context of the vowel considered and from the word ending, in a similar way to [1]. We propose a Discrimination Measure to analyse the discrimination power for the different input configurations and we validate this measure and present the results obtained in each case. For the best configuration we obtain a 94.9% proper names correctly stressed (5.1% error rate). These results are compared to similar experiments using a Memory based learning approach (kNearest Neighbours).