Comparing Word Sense Disambiguation and Distributional Models for Cross-Language Information Filtering

In this paper we deal with the problem of providing users with cross-language recommendations by comparing two dierent content- based techniques: the rst one relies on a knowledge-based word sense disambiguation algorithm that uses MultiWordNet as sense inventory, while the latter is based on the so-called distributional hypothesis and exploits a dimensionality reduction technique called Random Indexing in order to build language-independent user proles. This paper summarizes the results already presented within the confer- ence AI*IA 2011 [1].