%0 Conference Paper %1 FT005 %A Cohen, William W. %A Singer, Yoram %B 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval %C Zürich, CH %D 1996 %E Frei, Hans-Peter %E Harman, Donna %E Schäuble, Peter %E Wilkinson, Ross %I ACM Press, New York, US %K Text algorithms, categorization, context learning representation, %P 307--315 %T Context-sensitive learning methods for text categorization %U citeseer.nj.nec.com/cohen96contextsensitive.html

@inproceedings{FT005, added-at = {2009-06-22T10:05:09.000+0200}, address = {Z{\"{u}}rich, CH}, author = {Cohen, William W. and Singer, Yoram}, bdsk-url-1 = {citeseer.nj.nec.com/cohen96contextsensitive.html}, biburl = {https://www.bibsonomy.org/bibtex/25551b46f4869450c85eee5d5ee823a7f/n.nanas}, booktitle = {19th Annual International {ACM} SIGIR Conference on Research and Development in Information Retrieval}, editor = {Frei, Hans-Peter and Harman, Donna and Sch{\"{a}}uble, Peter and Wilkinson, Ross}, interhash = {7a1e8a84a3b4f310f519caa2ecc0eb61}, intrahash = {5551b46f4869450c85eee5d5ee823a7f}, keywords = {Text algorithms, categorization, context learning representation,}, pages = {307--315}, publisher = {ACM Press, New York, US}, qnote = {Very good paper. Both learning classifiers used are quite interesting and especially sleeping experts since RIPPER is more rule based. It has raised the importance of context which is also represented by my system. The learning mechanisms and weight updating algorithms are also of interest. In RIPPER the context is represented by a set of words in arbitrary order that are coexist in the same document. This is also what my representation is doing. The difference is that different "rules" are all incorporated in the same mode.}, timestamp = {2009-06-23T10:19:15.000+0200}, title = {Context-sensitive learning methods for text categorization}, url = {citeseer.nj.nec.com/cohen96contextsensitive.html}, year = 1996 }