Annalina Caputo

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The traditional strategy performed by Information Retrieval (IR) systems is ranked keyword search: For a given query, a list of documents , ordered by relevance, is returned. Relevance computation is primarily driven by a basic string-matching operation. To date, several attempts have been made to deviate from the traditional keyword search paradigm, often(More)
Distributional approaches are based on a simple hypothesis: the meaning of a word can be inferred from its usage. The application of that idea to the vector space model makes possible the construction of a WordSpace in which words are represented by mathematical points in a geometric space. Similar words are represented close in this space and the(More)
This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known variations of the Lesk WSD method. Given a word and its context, Lesk algorithm exploits the idea of maximum number of shared words (maximum overlaps) between the context of a word and each definition of its senses (gloss) in order to select the proper meaning.(More)
Information about top-ranked documents plays a key role to improve retrieval performance. One of the most common strategies which exploits this kind of information is relevance feedback. Few works have investigated the role of negative feedback on retrieval performance. This is probably due to the difficulty of dealing with the concept of non-relevant(More)
This paper presents evaluation experiments conducted at the University of Bari for the Ad-Hoc Robust WSD task of the Cross-Language Evaluation Forum (CLEF) 2008. The evaluation was performed using SENSE (SEmantic N-levels Search Engine) [2]. SENSE tries to overcome the limitations of the ranked keyword approach by introducing semantic levels, which(More)
In this paper we exploit Semantic Vectors to develop an IR system. The idea is to use semantic spaces built on terms and documents to overcome the problem of word ambiguity. Word ambiguity is a key issue for those systems which have access to textual information. Semantic Vectors are able to dividing the usages of a word into different meanings,(More)
A number of works have shown that the aggregation of several Information Retrieval (IR) systems works better than each system working individually. Nevertheless, early investigation in the context of CLEF Robust-WSD task, in which semantics is involved, showed that aggregation strategies achieve only slight improvements. This paper proposes a re-ranking(More)