PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Bayesian Network for XML Information Retrieval: Searching and Learning with the INEX Collection
Benjamin Piwowarski and Patrick Gallinari
Information Retrieval Volume 8, Number 4, pp. 655-681, 2005.

Abstract

Most recent document standards like XML 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. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents – which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1572
Deposited By:Patrick Gallinari
Deposited On:28 November 2005