Comparison of Word-based and Letter-based Text Classification

BOBICEV, Victoria

dc.contributor	Universitatea Tehnică a Moldovei
dc.contributor.author	BOBICEV, Victoria
dc.date.accessioned	2021-04-08T13:23:00Z
dc.date.available	2021-04-08T13:23:00Z
dc.date.issued	2007
dc.identifier.citation	BOBICEV, Victoria. Comparison of Word-based and Letter-based Text Classification. In: International Conference Recent Advances in Natural Language Processing: Proceedings, 27-29 Sept. 2007,Borovets, Bulgaria, 2007, pp. 76–80. Anthology ID R09-1075.	en_US
dc.identifier.uri	http://lml.bas.bg/ranlp2007/DOCS/RANLP2007.pdf
dc.identifier.uri	http://repository.utm.md/handle/5014/14049
dc.description	Access full text: http://lml.bas.bg/ranlp2007/DOCS/RANLP2007.pdf	en_US
dc.description.abstract	In this paper the comparison of two PPM (Prediction by Partial Matching) methods for automatic content-based text classification is described: on the basis of letters and on the basis of words. The investigation was driven by the idea that words and especially word combinations are more relevant features for many text classification tasks than letters and letter combinations. The results of the experiments proved applicability of PPM models for content-based text classification, although PPM model on the basis of words did not perform better than model on the basis of letters.	en_US
dc.language.iso	en	en_US
dc.publisher	Association for Computational Linguistics	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	prediction by partial matching	en_US
dc.subject	text classification	en_US
dc.subject	automatic text classification	en_US
dc.subject	content-based text classification	en_US
dc.title	Comparison of Word-based and Letter-based Text Classification	en_US
dc.type	Article	en_US