An Innovative Two-Stage WSD Unsupervised Method Un Innovador Método No Supervisado para Desambiguación de Sentidos de Palabras basado en dos etapas

An unsupervised method for word sense disambiguation is proposed. The sense of the word is chosen to be the most similar to the senses of other words that appear in the corpus in similar contexts. Training consists of building a weighted list of related words (quasi-synonyms) for each word; the weights are obtained by measuring similarity between the word’s contexts. We adapt the algorithm of McCarthy et al. 2004 for finding the best sense in each occurrence, instead of finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows then each quasi-synonym to accumulate a score for each ambiguous word sense; the sense with the highest score is chosen. We obtain a top precision of 69.86% using the same corpus for training and disambiguating.