Two-Stages Evaluation Algorithm for Automatic Ranking in Information Retrieval.
Conference: Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007, Volume II, June 25-28, 2007, Las Vegas, Nevada, USA
First, it is described a prototype on an Information Retrieval system, GUMSe. The system sends user queries to multiple search engines and it gets a final ordering of documents. In this paper an information fusion technique is presented. GUMSe retrieves and merges the resulting URLs in an efficient way. The size of the document collection to be analysed is limited by a previous algorithm called "fast". Afterwards each document is retrieved, an exhaustive algorithm evaluates each document analyzing its content. This algorithm uses n-grams to assign a weight to terms semantically related with the original user query terms. The final score is based in the score of the two previous approaches.
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ABSTRACT: Query expansion is one of the most complex tasks in information retrieval. Several new queries can be expanded related to a user one. The problem arises in choosing the queries that are more useful for search process. Here it is supposed that the most useful expanded queries are those queries which have similar meanings with regard to the original query but the number of words that they (original and expanded query) share is low. So, their meanings are similar but grammatically they are different. So, following this idea, several experiments have been carried out to assess a fuzzy measure that is able to select which are the most useful expanded queries, i.e., a fuzzy filtering process for query expansion.Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology - Workshops, 9-12 December 2008, Sydney, NSW, Australia; 01/2008
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