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  • × author_ss:"Chrisment, C."
  • × year_i:[2000 TO 2010}
  1. Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002) 0.00 
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    Abstract
    Boughanem, Chrisment, and Tamine use 144,186 documents and 25 queries from the TREC corpus AP88 to evaluate a genetic algorithm for multiple query evaluation against single query evaluation. They demonstrate niche construction by the use of a genetic technique to reproduce queries more often if they retrieve more relevant documents (genotypic sharing), or if they have close evaluation results (phenotypic sharing).New documents generated in each iteration are ranked by a merge based on one of these two principles. Genotypic sharing yields improvements of from 6% to 15% over single query evaluation, and phenotypic sharing shows from 5% to 15% improvement. Thus the niching technique appears to offer the possibility of successful merging of different query expressions.
    Type
    a
  2. Mothe, J.; Chrisment, C.; Dousset, B.; Alaux, J.: DocCube : Multi-dimensional visualisation and exploration of large document sets (2003) 0.00 
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    Abstract
    This paper presents a novel user interface that provides global visualizations of large document sets in order to help users to formulate the query that corresponds to their information needs and to access the corresponding documents. An important element of the approach we introduce is the use of concept hierarchies (CHs) in order to structure the document collection. Each CH corresponds to a facet of the documents users can be interested in. Users browse these CHs in order to specify and refine their information needs. Additionally the interface is based an OLAP principles and multidimensional analysis operators are provided to users in order to allow them to explore a document collection.
    Footnote
    Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"
    Type
    a
  3. Cabanac, G.; Chevalier, M.; Chrisment, C.; Julien, C.: Social validation of collective annotations : definition and experiment (2009) 0.00 
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    Abstract
    People taking part in argumentative debates through collective annotations face a highly cognitive task when trying to estimate the group's global opinion. In order to reduce this effort, we propose in this paper to model such debates prior to evaluating their social validation. Computing the degree of global confirmation (or refutation) enables the identification of consensual (or controversial) debates. Readers as well as prominent information systems may thus benefit from this information. The accuracy of the social validation measure was tested through an online study conducted with 121 participants. We compared their human perception of consensus in argumentative debates with the results of the three proposed social validation algorithms. Their efficiency in synthesizing opinions was demonstrated by the fact that they achieved an accuracy of up to 84%.
    Type
    a

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