Abstract
Work about distributional thesauri has now widely shown that the relations in these thesauri are mainly reliable for high frequency words and for capturing semantic relatedness rather than strict semantic similarity. In this article, we propose a method for improving such a thesaurus through its re-balancing in favor of middle and low frequency words. This method is based on a bootstrapping mechanism: a set of positive and negative examples of semantically related words are selected in a unsupervised way from the results of the initial measure and used for training a supervised classifier. This classifier is then applied for reranking the initial semantic neighbors. We evaluate the interest of this reranking for a large set of English nouns with various frequencies.
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