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].
Comparing Word Sense Disambiguation and Distributional Models for Cross-Language Information Filtering / Musto, Cataldo; Narducci, Fedelucio; Basile, Pierpaolo; Lops, Pasquale; de Gemmis, Marco; Semeraro, Giovanni. - ELETTRONICO. - 835:(2012), pp. 117-120. (Intervento presentato al convegno IIR 2012, Italian Information Retrieval Workshop tenutosi a Bari, Italy nel January 26-27, 2012).
Comparing Word Sense Disambiguation and Distributional Models for Cross-Language Information Filtering
Fedelucio Narducci;
2012-01-01
Abstract
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].I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
