A weighted maximum entropy language model for text classification

Fragos, K; Maistros, Y; Skourlas, C

dc.contributor.author	Fragos, K	en
dc.contributor.author	Maistros, Y	en
dc.contributor.author	Skourlas, C	en
dc.date.accessioned	2014-03-01T02:43:05Z
dc.date.available	2014-03-01T02:43:05Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31226
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-78651308482&partnerID=40&md5=2ac3ada742caf8fee55fd3699fca87c6	en
dc.relation.uri	http://www.informatik.uni-trier.de/~ley/db/conf/nlucs/nlucs2005.html#FragosMS05	en
dc.relation.uri	http://glotta.ntua.gr/nlp_lab/Fraggos/files/MaxEntropyTC.pdf	en
dc.relation.uri	http://glotta.ntua.gr/nlp/nlp_lab/Fraggos/files/MaxEntropyTC.pdf	en
dc.subject	Language Model	en
dc.subject	Maximum Entropy	en
dc.subject	Maximum Entropy Model	en
dc.subject	Natural Language Processing	en
dc.subject	part-of-speech tagging	en
dc.subject	Text Classification	en
dc.subject	Text Segmentation	en
dc.subject.other	Classification scheme	en
dc.subject.other	Classification tasks	en
dc.subject.other	Data sets	en
dc.subject.other	Feature words	en
dc.subject.other	Language model	en
dc.subject.other	Language modeling	en
dc.subject.other	Maximum entropy	en
dc.subject.other	Maximum entropy modeling	en
dc.subject.other	NAtural language processing	en
dc.subject.other	Part of speech tagging	en
dc.subject.other	Reuters-21578	en
dc.subject.other	State of the art	en
dc.subject.other	Text classification	en
dc.subject.other	Text segmentation	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Computational linguistics	en
dc.subject.other	Entropy	en
dc.subject.other	Feature extraction	en
dc.subject.other	Natural language processing systems	en
dc.subject.other	Statistical tests	en
dc.subject.other	Text processing	en
dc.title	A weighted maximum entropy language model for text classification	en
heal.type	conferenceItem	en
heal.publicationDate	2005	en
heal.abstract	The Maximum entropy (ME) approach has been extensively used in various Natural Language Processing tasks, such as language modeling, partof-speech tagging, text classification and text segmentation. Previous work in text classification was conducted using maximum entropy modeling with binary-valued features or counts of feature words. In this work, we present a method for applying Maximum Entropy modeling for text classification in a different way. Weights are used to select the features of the model and estimate the contribution of each extracted feature in the classification task. Using the X square test to assess the importance of each candidate feature we rank them and the most prevalent features, the most highly ranked, are used as the features of the model. Hence, instead of applying Maximum Entropy modeling in the classical way, we use the X square values to assign weights to the features of the model. Our method was evaluated on Reuters-21578 dataset for test classification tasks, giving promising results and comparably performing with some of the ""state of the art"" classification schemes.	en
heal.journalName	Proceedings of the 2nd International Workshop on Natural Language Understanding and Cognitive Science, NLUCS 2005, in Conjunction with ICEIS 2005	en
dc.identifier.spage	55	en
dc.identifier.epage	67	en