Improved Term Weighting Factors for Keyword Extraction in Hierarchical Category Structure and Thai Text Classification

2018 ◽  
pp. 58-67
Author(s):  
Boonthida Chiraratanasopha ◽  
Thanaruk Theeramunkong ◽  
Salin Boonbrahm
Keyword(s):  
Text Classification ◽  
Category Structure ◽  
Keyword Extraction ◽  
Term Weighting ◽  
Weighting Factors

scholarly journals Effect of Term Weighting on Keyword Extraction in Hierarchical Category Structure

2021 ◽  
Vol 40 (1) ◽  
pp. 57-82
Author(s):  
Boonthida Chiraratanasopha ◽  
Salin Boonbrahm ◽  
Thanaruk Theeramunkong
Keyword(s):  
Category Structure ◽  
Keyword Extraction ◽  
Term Weighting

scholarly journals Improving Term Weighting Schemes for Short Text Classification in Vector Space Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166578-166592
Author(s):  
Surender Singh Samant ◽  
N. L. Bhanu Murthy ◽  
Aruna Malapati
Keyword(s):  
Vector Space ◽  
Text Classification ◽  
Vector Space Model ◽  
Term Weighting ◽  
Weighting Schemes ◽  
Short Text ◽  
Space Model

A novel term weighting scheme for text classification: TF-MONO

2020 ◽  
Vol 14 (4) ◽  
pp. 101076 ◽  
Author(s):  
Turgut Dogan ◽  
Alper Kursat Uysal
Keyword(s):  
Text Classification ◽  
Weighting Scheme ◽  
Term Weighting

scholarly journals RANDOM WALK TERM WEIGHTING FOR IMPROVED TEXT CLASSIFICATION

2007 ◽  
Vol 01 (04) ◽  
pp. 421-439 ◽  
Author(s):  
SAMER HASSAN ◽  
RADA MIHALCEA ◽  
CARMEN BANEA
Keyword(s):  
Random Walk ◽  
Text Classification ◽  
Feature Weighting ◽  
Random Walk Model ◽  
Term Weighting ◽  
New Approach ◽  
Standard Classification ◽  
Term Weights ◽  
Traditional Term ◽  
Word Feature

This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier. The method uses term co-occurrence as a measure of dependency between word features. A random walk model is applied on a graph encoding words and co-occurrence dependencies, resulting in scores that represent a quantification of how a particular word feature contributes to a given context. Experiments performed on three standard classification datasets show that the new random walk based approach outperforms the traditional term frequency approach of feature weighting.

High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic

2012 ◽  
Vol 39 (1) ◽  
pp. 1147-1155 ◽  
Author(s):  
Lam Hong Lee ◽  
Dino Isa ◽  
Wou Onn Choo ◽  
Wen Yeen Chue
Keyword(s):  
Text Classification ◽  
Keyword Extraction

scholarly journals Hierarchical text classification using Relative Inverse Document Frequency

2021 ◽  
Vol 15 (2) ◽  
pp. 166-176
Author(s):  
Boonthida Chiraratanasopha ◽  
Thanaruk Theeramunkong ◽  
Salin Boonbrahm
Keyword(s):  
Text Classification ◽  
Term Weighting ◽  
Hierarchical Tree ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Relative Inverse ◽  
The Hierarchical Structure ◽  
Family Based ◽  
Hierarchical Text Classification

Automatic hierarchical text classification has been a challenging and in-needed task with an increasing of hierarchical taxonomy from the booming of knowledge organization. The hierarchical structure identifies the relationships of dependence between different categories in which can be overlapped of generalized and specific concepts within the tree. This paper presents the use of frequency of the occurring term in related categories among the hierarchical tree to help in document classification. The four extended term weighting of Relative Inverse Document Frequency (IDFr) including its located category, its parent category, its sibling categories and its child categories are exploited to generate a classifier model using centroid-based technique. From the experiment on hierarchical text classification of Thai documents, the IDFr achieved the best accuracy and F-measure as 53.65% and 50.80% in Top-n features set from family-based evaluation in which are higher than TF-IDF for 2.35% and 1.15% in the same settings, respectively.

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