The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Local and Global Latent Semantic Analysis for Text Categorization
Abstract
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.
Related Content
|
Hrithik Raj, Ritu Punhani, Ishika Punhani.
© 2023.
31 pages.
|
|
Divi Anand, Isha Kaushik, Jasmehar Singh Mann, Ritu Punhani, Ishika Punhani.
© 2023.
21 pages.
|
|
Jayanthi G., Purushothaman R..
© 2023.
10 pages.
|
|
Anshika Gupta, Shuchi Sirpal.
© 2023.
14 pages.
|
|
Reet Kaur Kohli, Seneha Santoshi, Sunishtha S. Yadav, Vandana Chauhan.
© 2023.
13 pages.
|
|
Poonam Tanwar.
© 2023.
14 pages.
|
|
Monika Mehta, Shivani Mishra, Santosh Kumar, Muskaan Bansal.
© 2023.
16 pages.
|
|
|