Statistical relational learning for document mining

@misc{citeulike:71559, abstract = {A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most real-world databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored in relational databases. Potential features are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present...}, author = {Popescul, A. and Ungar, L. and Lawrence, S. and Pennock, D. }, citeulike-article-id = {71559}, keywords = {document-mining, relational-learning, survey, text-classification}, posted-at = {2007-06-11 09:32:41}, priority = {2}, title = {Statistical relational learning for document mining}, url = {http://citeseer.ist.psu.edu/popescul03statistical.html}, year = {2003} }

TY - GEN ID - citeulike:71559 L3 - citeulike-article-id:71559 TI - Statistical relational learning for document mining N2 - A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most real-world databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored in relational databases. Potential features are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present... KW - document-mining KW - relational-learning KW - survey KW - text-classification AU - Popescul, A AU - Ungar, L AU - Lawrence, S AU - Pennock, D PY - 2003/// UR - http://citeseer.ist.psu.edu/popescul03statistical.html ER -