Most Natural Language Processing (NLP) systems traditionally use a sequential architecture that represents the classical linguistic levels (lexical-morphological, syntactical, semantic, etc.), but they claim for huge and monolithic modules, that make the backtracking among such modules difficult. By using a…

This paper focuses on the general problem of the lexical bottleneck and, in particular, on the issues of semantic clustering and disambiguation by means of word usage cues obtained from sublanguage-specific corpora. Our approaches combines the use of numerical techniques…

In this paper we introduce a probabilistic approach to support visual supervision and gesture recognition. Task knowledge is both of geometric and visual nature and it is encoded in parametric eigenspaces. Learning processes for compute modal subspaces (eigenspaces) are the…