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Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies

Domain:

natural language processing

Record type:

paper
Creator:
AbbSmaBer
Editor:
CenAnaStaDep
Publisher:
CCSD
Host:avatar
International audience The aim of this study is topic identification byusing two methods, in this case, a new one that we haveproposed: TR-classifier which is based on computingtriggers, and the well-known k Nearest Neighbors.Performances are acceptable, particularly for TR-classifier,though we have used reduced sizes of vocabularies. For theTR-Classifier, each topic is represented by a vocabularywhich has been built using the corresponding trainingcorpus. Whereas, the kNN method uses a generalvocabulary, obtained by the concatenation of those used bythe TR-Classifier. For the evaluation task, six topics havebeen selected to be identified: Culture, religion, economy,local news, international news and sports. An Arabic corpushas been used to achieve experiments.

Visit

hal.science

Tasks

text classificationtopic classification

Tags

TR-Classifier[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]

Licenses

https://about.hal.science/hal-authorisation-v1/info:eu-repo/semantics/OpenAccess

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