A Machine Learning Model for Information Retrieval with Structured Documents

Benjamin Piwowarski 1 Patrick Gallinari 1
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Most recent document standards rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. Only a few models have been proposed for handling structured documents, and the design of such systems is still an open problem. We present here a new model for structured document retrieval which allows to compute and to combine the scores of document parts. It is based on bayesian networks and allows for learning the model parameters in the presence of incomplete data. We present an application of this model for ad-hoc retrieval and evaluate its performances on a small structured collection. The model can also be extended to cope with other tasks such as interactive navigation in structured documents or corpus.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01532271
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Submitted on : Friday, June 2, 2017 - 4:13:44 PM
Last modification on : Thursday, March 21, 2019 - 1:07:32 PM

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Benjamin Piwowarski, Patrick Gallinari. A Machine Learning Model for Information Retrieval with Structured Documents. MLDM 2003 - 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition, Jul 2003, Leipzig, Germany. pp.425-438, ⟨10.1007/3-540-45065-3_37⟩. ⟨hal-01532271⟩

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