Name
Abstract | ||
|---|---|---|
In databases or in the World Wide Web, many documents are in a structured format (e.g. XML). We propose in this article to extend the classical IR probabilistic model in order to take into account the structure through the weighting of tags. Our approach includes a learning step in which the weight of each tag is computed. This weight estimates the probability that the tag distinguishes the terms which are the most relevant. Our model has been evaluated on a large collection during INEX IR evaluation campaigns. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/WIIAT.2008.346 | Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference |
Keywords | Field | DocType |
information retrieval,learning (artificial intelligence),probability,World Wide Web,classical information retrieval probabilistic model,document retrieval,integration structure,learning step,weight estimation,XML,probabilistic model,structure,tags | Data mining,Divergence-from-randomness model,IR evaluation,Weighting,XML,Information retrieval,Computer science,Search engine indexing,Statistical model,Probabilistic logic,The Internet | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3496-1 | 5 |
PageRank | References | Authors |
0.49 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathias Gery | 1 | 5 | 0.49 |
| Christine Largeron | 2 | 5 | 3.19 |
| Franck Thollard | 3 | 227 | 17.10 |