text information retrieval
Recently Published Documents


TOTAL DOCUMENTS

50
(FIVE YEARS 2)

H-INDEX

7
(FIVE YEARS 0)

2021 ◽  
Author(s):  
Sherlon Almeida da Silva ◽  
Evangelos E. Milios ◽  
Maria Cristina F. de Oliveira


2021 ◽  
Vol 36 (3) ◽  
pp. 861-892
Author(s):  
abolfazl Asadnia ◽  
Mehrdad CheshmehSohrabi ◽  
Ahmad shaban ◽  
عاصفه عاصمی ◽  
Mohsen Taheri Demneh ◽  
...  


2020 ◽  
Vol 195 ◽  
pp. 105679
Author(s):  
Zongda Wu ◽  
Shigen Shen ◽  
Xinze Lian ◽  
Xinning Su ◽  
Enhong Chen


2020 ◽  
Vol 168 ◽  
pp. 123-128
Author(s):  
Ishita Daga ◽  
Anchal Gupta ◽  
Raj Vardhan ◽  
Partha Mukherjee




2019 ◽  
Vol 71 (3) ◽  
pp. 349-369 ◽  
Author(s):  
Jingjing Liu ◽  
Chang Liu ◽  
Nicholas J. Belkin


2018 ◽  
Vol 5 (4) ◽  
pp. 32-47 ◽  
Author(s):  
Nidhika Yadav ◽  
Niladri Chatterjee

Information retrieval is widely used due to extremely large volume of text and image data available on the web and consequently, efficient retrieval is required. Text information retrieval is a branch of information retrieval which deals with text documents. Another key factor is the concern for a retrieval engine, often referred to as user-specific information retrieval, which works according to a specific user. This article performs a preliminary investigation of the proposed fuzzy rough sets-based model for user-specific text information retrieval. The model improves on the computational time required to compute the approximations compared to classical fuzzy rough set model by using Wikipedia as the information source. The technique also improves on the accuracy of clustering obtained for user specified classes.





Sign in / Sign up

Export Citation Format

Share Document