Application of a New Set of Pseudo-Distances in Documents Categorization

Title:

Application of a New Set of Pseudo-Distances in Documents Categorization

Creator:

Gadri, S. and Moussaoui, A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:297b9ed4-a03c-414c-b061-161fcac32191
uuid:297b9ed4-a03c-414c-b061-161fcac32191
doi:10.14311/NNW.2017.27.011

Subject:

N -grams, language identification, text categorization, text mining, machine learning, Kullback-Leibler distance, X2 distance, and Cavnar-Trenkle distance

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Automatic text classification is a very important task that consists in assigning labels (categories, groups, classes) to a given text based on a set of previously labeled texts called training set. The work presented in this paper treats the problem of automatic topical text categorization. It is a supervised classification because it works on a predefined set of classes and topical because it uses topics or subjects of texts as classes. In this context, we used a new approach based on $k$-NN algorithm, as well as a new set of pseudo-distances (distance metrics) known in the field of language identification. We also proposed a simple and effective method to improve the quality of performed categorization.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:2

Harvested from:

CDK

Metadata only:

false