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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Piwowarski, B., Gallinari, P. (2003). A Machine Learning Model for Information Retrieval with Structured Documents. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_37
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DOI: https://doi.org/10.1007/3-540-45065-3_37
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