Latent semantic analysis for text categorization using neural network

2008 ◽  
Vol 21 (8) ◽  
pp. 900-904 ◽  
Author(s):  
Bo Yu ◽  
Zong-ben Xu ◽  
Cheng-hua Li
Keyword(s):  
Neural Network ◽  
Latent Semantic Analysis ◽  
Text Categorization ◽  
Semantic Analysis

Local and Global Latent Semantic Analysis for Text Categorization

2014 ◽  
Vol 4 (3) ◽  
pp. 1-13
Author(s):  
Khadoudja Ghanem
Keyword(s):  
Information Retrieval ◽  
High Precision ◽  
Latent Semantic Analysis ◽  
Classification Accuracy ◽  
Text Categorization ◽  
Semantic Analysis ◽  
Experimental Results ◽  
Semantic Approach ◽  
Document Categorization ◽  
Second Use

In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.

Local and Global Latent Semantic Analysis for Text Categorization

2018 ◽  
pp. 1360-1374 ◽  
Author(s):  
Khadoudja Ghanem
Keyword(s):  
Information Retrieval ◽  
High Precision ◽  
Latent Semantic Analysis ◽  
Classification Accuracy ◽  
Text Categorization ◽  
Semantic Analysis ◽  
Experimental Results ◽  
New Method ◽  
Semantic Approach ◽  
Document Categorization

In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.

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