caa a22 4500 463251350 CHVBK 20180405153341.0 cr unu---uuuuu 170326e20070101xx s 000 0 eng 10.1007/s10791-006-9004-6 doi (NATIONALLICENCE)springer-10.1007/s10791-006-9004-6 Extending WHIRL with background knowledge for improved text classification [Elektronische Daten] [Sarah Zelikovitz, William Cohen, Haym Hirsh] Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web. Springer Science + Business Media, LLC, 2006 Text categorization nationallicence Background knowledge nationallicence Semi-supervised learning nationallicence Information retrieval nationallicence Query processing nationallicence Zelikovitz Sarah Computer Science Department, College of Staten Island of CUNY, 2800 Victory Blvd, 10314, Staten Island, NY, USA aut Cohen William Center for Automated Learning and Discovery, Carnegie Mellon University, 500 Forbes Avenue, 15213, Pittsburgh, PA, USA aut Hirsh Haym Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA aut Information Retrieval Kluwer Academic Publishers 10/1(2007-01-01), 35-67 1386-4564 10:1<35 2007 10 10791 https://doi.org/10.1007/s10791-006-9004-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10791-006-9004-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zelikovitz Sarah Computer Science Department, College of Staten Island of CUNY, 2800 Victory Blvd, 10314, Staten Island, NY, USA aut NATIONALLICENCE 700 1- Cohen William Center for Automated Learning and Discovery, Carnegie Mellon University, 500 Forbes Avenue, 15213, Pittsburgh, PA, USA aut NATIONALLICENCE 700 1- Hirsh Haym Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA aut NATIONALLICENCE 773 0- Information Retrieval Kluwer Academic Publishers 10/1(2007-01-01), 35-67 1386-4564 10:1<35 2007 10 10791 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer