Fabio Clarizia

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It is well known that one way to improve the accuracy of a text retrieval system is to expand the original query with additional knowledge coded through topic-related terms. In the case of an interactive environment, the expansion, which is usually represented as a list of words, is extracted from documents whose relevance is known thanks to the feedback of(More)
Text classification methods have been evaluated on supervised classification tasks of large datasets showing high accuracy. Nevertheless, due to the fact that these classifiers, to obtain a good performance on a test set, need to learn from many examples, some difficulties may be found when they are employed in real contexts. In fact, most users of a(More)
In this paper we address the problem of modeling large collections of data, namely web pages by exploiting jointly traditional information retrieval techniques with probabilistic ones in order to find semantic descriptions for the collections. This novel technique is embedded in a real Web Search Engine in order to provide semantics functionalities, as(More)
The experience of a touristic visit is a learning process very fascinating and interesting: the emotions can change according to the interests of the indi‐ viduals, as well as of the physical, personal and social-cultural context. Applica‐ tions for a mobile environment should take advantage of contextual information, such as position, to offer greater(More)
It has been demonstrated that a way to increase the number of relevant documents returned by an informational query performed on a Web repository is to expand the original query with additional knowledge, for instance coded through other topic-related terms. In this paper we propose a new technique to build automatically, through the probabilistic topic(More)