Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Relying on the Cluster Hypothesis, which states that relevant documents tend to be more similar one to each other than to non-relevant ones, most of information retrieval systems p...
Sylvain Lamprier, Tassadit Amghar, Bernard Levrat,...
We present a method and a software tool, the FrameNet Transformer, for deriving customized versions of the FrameNet database based on frame and frame element relations. The FrameN...
This paper presents an algorithm for unsupervised noun sense induction, based on clustering of Web search results. The algorithm does not utilize labeled training instances or any...
Goldee Udani, Shachi Dave, Anthony Davis, Tim Sibl...
We address here the need to assist users in rapidly accessing the most important or strategic information in the text corpus by identifying sentences carrying specific information...