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Communication Dans Un Congrès Année : 2005

Fuzzy Proximity Ranking with Boolean Queries

Résumé

Based on the idea that the closer the query terms are in a document, the more relevant this document is, we experiment an IR method based on a fuzzy proximity degree of the query term occurences in a document to compute its relevance to the query. Our model is able to deal with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an in uence function. Given a query term and a document, the in uence function associates to each position in the text a value dependant on the distance of the nearest occurence of this query term. To model proximity, this function is decreasing with distance. Di erent forms of function can be used: triangular, gaussian etc. For practical reasons only functions with nite support were used. The support of the function is limited by a constant called k. The fuzzy term proximity functions are associated to every leaves of the query tree. Then fuzzy proximities are computed for every nodes with a post-order tree traversal. Given the fuzzy proximities of the sons of a node, its fuzzy proximity is computed, like in the fuzzy IR models, with a mimimum (resp. maximum) combination for conjunctives (resp. disjunctives) nodes. Finally, a fuzzy query proximity value is obtained for each position in this document at the root of the query tree. The score of this document is the integration of the function obtained at the tree root. For the experiments, we modi ed Lucy (version 0.5.2) to implement our IR model. Three query sets are used for our eight runs. One set is manually built with the title words and some description words. Each of these words is OR'ed with its derivatives like plurals for instance. Then the OR nodes obtained are AND'ed at the tree root. The two automatic query sets are built with an AND of automatically extracted terms from either the title eld or the description eld. These three query sets are submitted to our system with two values of k: 50 and 200. As our method is aimed at high precision, it sometimes give less than one thousand answers. In such cases, the documents retrieved by the BM-25 method implemented in Lucy was concatenated after our result list.
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Dates et versions

hal-00406901 , version 1 (23-07-2009)

Identifiants

  • HAL Id : hal-00406901 , version 1

Citer

Michel Beigbeder, Annabelle Mercier. Fuzzy Proximity Ranking with Boolean Queries. Fourteenth Text REtrieval Conference (TREC 2005), Nov 2005, Gaithersburg, Maryland, United States. 10p. ⟨hal-00406901⟩
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