A Query Expansion Method Using Multinomial Naive Bayes

Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are...

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Autores principales:	Sergio Silva, Adrián Seara Vieira, Pedro Celard, Eva Lorenzo Iglesias, Lourdes Borrajo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	query expansion information retrieval multinomial naive bayes relevance feedback Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7b08ed33fcc34dd4a6151ea458b11ac3
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id	oai:doaj.org-article:7b08ed33fcc34dd4a6151ea458b11ac3
record_format	dspace
spelling	oai:doaj.org-article:7b08ed33fcc34dd4a6151ea458b11ac32021-11-11T15:19:03ZA Query Expansion Method Using Multinomial Naive Bayes10.3390/app1121102842076-3417https://doaj.org/article/7b08ed33fcc34dd4a6151ea458b11ac32021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10284https://doaj.org/toc/2076-3417Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are relevant to the user requirements. The user initial query is reformulated, adding meaningful terms with similar significance. In this study, a supervised query expansion technique based on an innovative use of the Multinomial Naive Bayes to extract relevant terms from the first documents retrieved by the initial query is presented. The proposed method was evaluated using MAP and R-prec on the first 5, 10, 15, and 100 retrieved documents. The improved performance of the expanded queries increased the number of relevant retrieved documents in comparison to the baseline method. We achieved more accurate document retrieval results (MAP 0.335, R-prec 0.369, P5 0.579, P10 0.469, P15 0.393, P100 0.175) as compared to the top performers in TREC2017 Precision Medicine Track.Sergio SilvaAdrián Seara VieiraPedro CelardEva Lorenzo IglesiasLourdes BorrajoMDPI AGarticlequery expansioninformation retrievalmultinomial naive bayesrelevance feedbackTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10284, p 10284 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	query expansion information retrieval multinomial naive bayes relevance feedback Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	query expansion information retrieval multinomial naive bayes relevance feedback Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Sergio Silva Adrián Seara Vieira Pedro Celard Eva Lorenzo Iglesias Lourdes Borrajo A Query Expansion Method Using Multinomial Naive Bayes
description	Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are relevant to the user requirements. The user initial query is reformulated, adding meaningful terms with similar significance. In this study, a supervised query expansion technique based on an innovative use of the Multinomial Naive Bayes to extract relevant terms from the first documents retrieved by the initial query is presented. The proposed method was evaluated using MAP and R-prec on the first 5, 10, 15, and 100 retrieved documents. The improved performance of the expanded queries increased the number of relevant retrieved documents in comparison to the baseline method. We achieved more accurate document retrieval results (MAP 0.335, R-prec 0.369, P5 0.579, P10 0.469, P15 0.393, P100 0.175) as compared to the top performers in TREC2017 Precision Medicine Track.
format	article
author	Sergio Silva Adrián Seara Vieira Pedro Celard Eva Lorenzo Iglesias Lourdes Borrajo
author_facet	Sergio Silva Adrián Seara Vieira Pedro Celard Eva Lorenzo Iglesias Lourdes Borrajo
author_sort	Sergio Silva
title	A Query Expansion Method Using Multinomial Naive Bayes
title_short	A Query Expansion Method Using Multinomial Naive Bayes
title_full	A Query Expansion Method Using Multinomial Naive Bayes
title_fullStr	A Query Expansion Method Using Multinomial Naive Bayes
title_full_unstemmed	A Query Expansion Method Using Multinomial Naive Bayes
title_sort	query expansion method using multinomial naive bayes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7b08ed33fcc34dd4a6151ea458b11ac3
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A Query Expansion Method Using Multinomial Naive Bayes

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