20 research outputs found
Learning Term Weights for Ad-hoc Retrieval
Most Information Retrieval models compute the relevance score of a document
for a given query by summing term weights specific to a document or a query.
Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used
to specify how a term weight is computed. In this paper, we propose to leverage
learning-to-rank principles to learn how to compute a term weight for a given
document based on the term occurrence pattern
Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System
International audienceThis paper tackles the problem of pinpointing relevant information in a social network for Personalized Information Retrieval (PIR). We start from the premise that user profiles must be filtered so that they outperform non profile based queries. The formal Profile Query Expansion Constraint is then defined. We fix a specific integration of profile and a probabilistic matching framework that fits into the constraint defined. Experiments are conducted on the Bibson-omy corpus. Our findings show that even simple profile adaptation using query is effective for Personalized Information Retrieval
Contrainte de correspondance Document-Document pour la RI. Application Ă la Divergence de Kullback-Leibler
National audienceThis paper defines a new axiomatic constraint, namely DDMC (Document-DocumentMatching Constraint), for information retrieval that depicts the behavior of a matching if a corpusdocument is used as a query. The DDMC constraint is not verified by a classical IR modellike the Language Model based on Jelinek-Mercer smoothing and Kulback-Leibler Divergence.We introduce a modification of this model to validate DDMC. An experiment conducted on twocorpus show that the modification of the reference model does not degrade significantly theresults, and validates the DDMC.Cet article décrit une contrainte d'un modèle de recherche d'information décrivant les comportement attendu d'un système si un document du corpus est posé en requête, la contrainte DDMC (Document-Document Matching Constraint). Cette contrainte n'étant pas vérifiée par un modèle classique de recherche d'information (modèle de langue basé sur un calcul de néga-tive de Divergence de Kullback-Leibler avec lissage de Jelinek-Mercer), nous présentons une modification de ce dernier modèle qui permet de vérifier DDMC. Une dernière partie présente des expérimentations menées afin de vérifier que notre modification n'impacte pas la qualité des réponses d'un système, tout en garantissant la vérification de DDMC
Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View
Multimedia collections are more than ever growing in size and diversity.
Effective multimedia retrieval systems are thus critical to access these
datasets from the end-user perspective and in a scalable way. We are interested
in repositories of image/text multimedia objects and we study multimodal
information fusion techniques in the context of content based multimedia
information retrieval. We focus on graph based methods which have proven to
provide state-of-the-art performances. We particularly examine two of such
methods : cross-media similarities and random walk based scores. From a
theoretical viewpoint, we propose a unifying graph based framework which
encompasses the two aforementioned approaches. Our proposal allows us to
highlight the core features one should consider when using a graph based
technique for the combination of visual and textual information. We compare
cross-media and random walk based results using three different real-world
datasets. From a practical standpoint, our extended empirical analysis allow us
to provide insights and guidelines about the use of graph based methods for
multimodal information fusion in content based multimedia information
retrieval.Comment: An extended version of the paper: Visual and Textual Information
Fusion in Multimedia Retrieval using Semantic Filtering and Graph based
Methods, by J. Ah-Pine, G. Csurka and S. Clinchant, submitted to ACM
Transactions on Information System