Text Classification by Boosting Weak Learners based on Terms and Concepts Export

@inproceedings{citeulike:1371262, abstract = {Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.}, address = {Washington, DC, USA}, author = {Bloehdorn, Stephan and Hotho, Andreas}, booktitle = {ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04)}, citeulike-article-id = {1371262}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1032649.1033473}, isbn = {0769521428}, keywords = {classification--automatic, classification--text--automated, machine\_learning}, pages = {331--334}, posted-at = {2007-06-07 21:02:46}, priority = {2}, publisher = {IEEE Computer Society}, title = {Text Classification by Boosting Weak Learners based on Terms and Concepts}, url = {http://portal.acm.org/citation.cfm?id=1032649.1033473}, year = {2004} }

TY - CONF ID - citeulike:1371262 L3 - citeulike-article-id:1371262 N2 - Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results. SN - 0769521428 AD - Washington, DC, USA EP - 334 TI - Text Classification by Boosting Weak Learners based on Terms and Concepts PB - IEEE Computer Society T2 - ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04) SP - 331 KW - classification--automatic KW - classification--text--automated KW - machine_learning AU - Bloehdorn, Stephan AU - Hotho, Andreas PY - 2004/// UR - http://portal.acm.org/citation.cfm?id=1032649.1033473 ER -