Name
Abstract | ||
|---|---|---|
Starting from the idea that the closer the query terms in a document are to each other the more relevant the document, we propose an information retrieval method that uses the degree of fuzzy proximity of key terms in a document to compute the relevance of the document to the query. Our model handles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to control the proximity degree required. To improve our system we use a stemming algorithm before indexing, we take a specific influence function and we merge fuzzy proximity result list built with different spread of influence function. We explain how we construct the queries and report the results of our experiments in the ad-hoc monolingual French task of the CLEF 2006 evaluation campaign. |
Year | Venue | Keywords |
|---|---|---|
2006 | CLEF (Working Notes) | fuzzy information retrieval,term proximity,indexation,information retrieval |
Field | DocType | Citations |
Data mining,Fuzzy information retrieval,Information retrieval,Computer science,Fuzzy logic,Fuzzy set,Operator (computer programming),Proximity search,Vector space model,Term Discrimination,Clef | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Annabelle Mercier | 1 | 41 | 8.72 |
| Michel Beigbeder | 2 | 4 | 1.26 |