A literature review on question answering techniques, paradigms and systems
Background: Question Answering (QA) systems enable users to retrieve exact answers for questions posed in natural language. Objective: This study aims at identifying QA techniques, tools and systems, as well as the metrics and indicators used to measure these approaches for QA systems and also to de...
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doaj-af5540b89a1f4a85ab189fd9c1caa8512020-11-25T03:46:07ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782020-07-01326635646A literature review on question answering techniques, paradigms and systemsMarco Antonio Calijorne Soares0Fernando Silva Parreiras1Corresponding author.; LAIS – Laboratory for Advanced Information Systems, FUMEC University, Av. Afonso Pena 3880, 30130 009 Belo Horizonte, MG, BrazilLAIS – Laboratory for Advanced Information Systems, FUMEC University, Av. Afonso Pena 3880, 30130 009 Belo Horizonte, MG, BrazilBackground: Question Answering (QA) systems enable users to retrieve exact answers for questions posed in natural language. Objective: This study aims at identifying QA techniques, tools and systems, as well as the metrics and indicators used to measure these approaches for QA systems and also to determine how the relationship between Question Answering and natural language processing is built. Method: The method adopted was a Systematic Literature Review of studies published from 2000 to 2017. Results: 130 out of 1842 papers have been identified as describing a QA approach developed and evaluated with different techniques. Conclusion: Question Answering researchers have concentrated their efforts in natural language processing, knowledge base and information retrieval paradigms. Most of the researches focused on open domain. Regarding the metrics used to evaluate the approaches, Precision and Recall are the most addressed.http://www.sciencedirect.com/science/article/pii/S131915781830082XQuestion answering systemsNatural language processingInformation retrieval |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marco Antonio Calijorne Soares Fernando Silva Parreiras |
spellingShingle |
Marco Antonio Calijorne Soares Fernando Silva Parreiras A literature review on question answering techniques, paradigms and systems Journal of King Saud University: Computer and Information Sciences Question answering systems Natural language processing Information retrieval |
author_facet |
Marco Antonio Calijorne Soares Fernando Silva Parreiras |
author_sort |
Marco Antonio Calijorne Soares |
title |
A literature review on question answering techniques, paradigms and systems |
title_short |
A literature review on question answering techniques, paradigms and systems |
title_full |
A literature review on question answering techniques, paradigms and systems |
title_fullStr |
A literature review on question answering techniques, paradigms and systems |
title_full_unstemmed |
A literature review on question answering techniques, paradigms and systems |
title_sort |
literature review on question answering techniques, paradigms and systems |
publisher |
Elsevier |
series |
Journal of King Saud University: Computer and Information Sciences |
issn |
1319-1578 |
publishDate |
2020-07-01 |
description |
Background: Question Answering (QA) systems enable users to retrieve exact answers for questions posed in natural language. Objective: This study aims at identifying QA techniques, tools and systems, as well as the metrics and indicators used to measure these approaches for QA systems and also to determine how the relationship between Question Answering and natural language processing is built. Method: The method adopted was a Systematic Literature Review of studies published from 2000 to 2017. Results: 130 out of 1842 papers have been identified as describing a QA approach developed and evaluated with different techniques. Conclusion: Question Answering researchers have concentrated their efforts in natural language processing, knowledge base and information retrieval paradigms. Most of the researches focused on open domain. Regarding the metrics used to evaluate the approaches, Precision and Recall are the most addressed. |
topic |
Question answering systems Natural language processing Information retrieval |
url |
http://www.sciencedirect.com/science/article/pii/S131915781830082X |
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