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Luppar: Information Retrieval for Closed Text Document Collections

by Fabiano Tavares da Silva, Jose Everardo Bessa Maia
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 28
Year of Publication: 2020
Authors: Fabiano Tavares da Silva, Jose Everardo Bessa Maia
10.5120/ijais2020451846

Fabiano Tavares da Silva, Jose Everardo Bessa Maia . Luppar: Information Retrieval for Closed Text Document Collections. International Journal of Applied Information Systems. 12, 28 ( March 2020), 1-6. DOI=10.5120/ijais2020451846

@article{ 10.5120/ijais2020451846,
author = { Fabiano Tavares da Silva, Jose Everardo Bessa Maia },
title = { Luppar: Information Retrieval for Closed Text Document Collections },
journal = { International Journal of Applied Information Systems },
issue_date = { March 2020 },
volume = { 12 },
number = { 28 },
month = { March },
year = { 2020 },
issn = { 2249-0868 },
pages = { 1-6 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number28/1079-2020451846/ },
doi = { 10.5120/ijais2020451846 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:10:17.718555+05:30
%A Fabiano Tavares da Silva
%A Jose Everardo Bessa Maia
%T Luppar: Information Retrieval for Closed Text Document Collections
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 28
%P 1-6
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article presents Luppar, an Information Retrieval tool for closed collections of text documents which uses a local distributional semantic model associated to each corpus. The system performs automatic query expansion using a combination of distributional semantic model and local context analysis and supports relevancy feedback. The performance of the system was evaluated in databases of different domains and presented results equal to or higher than those published in the literature.

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Index Terms

Computer Science
Information Sciences

Keywords

Information Retrieval Distributional Semantic Model Local Context Analysis Closed Document Collection

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