Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies
Domaine:
natural language processingType de record:
paper
Créateur:
AbbSmaBer
Éditeur:
CenAnaStaDep
Éditeur:
CCSD
Hôte:
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.
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