Cross-language Information Filtering: Word Sense Disambiguation vs. Distributional Models

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as relevant documents written in different languages from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. How could we represent user information needs or user preferences in a language-independent way? In this paper, we compared two content-based techniques able to provide users with cross-language recommendations: the first one relies on a knowledge-based word sense disambiguation technique that uses MultiWordNet as sense inventory, while the latter is based on a dimensionality reduction technique called Random Indexing and exploits the so-called distributional hypothesis in order to build language-independent user profiles. Since the experiments conducted in a movie recommendation scenario show the effectiveness of both approaches, we tried also to underline strenghts and weaknesses of each approach in order to identify scenarios in which a specific technique fits better.