Early and late combinations of criteria for reranking distributional thesauri
Abstract
In this article, we first propose to exploit a new criterion for improving distributional thesauri. Following a bootstrapping perspective, we select relations between the terms of similar nominal compounds for building in an unsupervised way the training set of a classifier performing the reranking of a thesaurus. Then, we evaluate several ways to combine thesauri reranked according to different criteria and show that exploiting the complementary information brought by these criteria leads to significant improvements.