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Content-Based News Retrieval on the Web


Pasquale Capasso, Carmine Cesarano, Antonio Picariello, Lucio Sansone


Vol. 6  No. 5  pp. 68-94


Effective search and retrieval are fundamental for realizing the full potential of the Web. Although nowadays search engines perform much better than few years ago, big improvements are still needed with respect to the relevance of the retrieved documents to the user's query and the presentation of the results. In this paper we present a novel retrieval system which exploits Wordnet's semantics in identifying the topic of News documents and ranks the retrieved ones according to their relevance to the query. Furthermore, the system provides the user with a short summary of each document, helping her/him in browsing the result set.


Information Retrieval, Relevant Terms extraction, NPL, clustering