We apply a well-known Bayesian probabilistic model to textual information retrieval: the classification of documents based on their relevance to a query. This model was previously...
Named entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories. Although extensive research has focused on the de...
Linda tuple spaces are flat and unstructured, in the sense that they do not allow for expressing preferences of tuples; for example, we could be interested in indicating tuples th...
Mario Bravetti, Roberto Gorrieri, Roberto Lucchi, ...
We present a probabilistic model for a document corpus that combines many of the desirable features of previous models. The model is called “GaP” for Gamma-Poisson, the distri...
This paper proposes a demo of the TopX search engine, an extensive framework for unified indexing, querying, and ranking of large collections of unstructured, semistructured, and ...