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107513

Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization

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Last updated: 26 Dec 2024

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Abstract

Many document retrieval methods focusing on unstructured text to deliver more meaningful information on the user. Tag-based document retrieval aims to address a challenge to searching relevant text-documents given a set of tags. Tag-based approaches received a wide attention as a possible solution to the big-content related IR, showing a high performance through a combination of its effectiveness and efficiency. This paper use word sense  isambiguation with non-negative matrix factorization to generate topic model based semantic.

DOI

10.21608/fcihib.2019.107513

Keywords

semantic tagging, topic model, semantic document retrieval, non-negative matrix factorization

Authors

First Name

Fatma

Last Name

Sayed Gadelrab

MiddleName

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Affiliation

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Orcid

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First Name

Mohammed

Last Name

H. Haggag

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Affiliation

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Orcid

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First Name

Rowayda

Last Name

A. Sadek

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Volume

1

Article Issue

1

Related Issue

16264

Issue Date

2019-01-01

Receive Date

2019-01-19

Publish Date

2019-01-19

Page Start

29

Page End

34

Print ISSN

2537-0901

Online ISSN

2535-1397

Article File

FCIHIB_Volume 1_Issue 1_Pages 29-34.pdf

PDF . 440.6kB

Link

https://fcihib.journals.ekb.eg/article_107513.html

Detail API

https://fcihib.journals.ekb.eg/service?article_code=107513

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3

Type

المقالة الأصلية

Type Code

1,411

Publication Type

Journal

Publication Title

النشرة المعلوماتية في الحاسبات والمعلومات

Publication Link

https://fcihib.journals.ekb.eg/

MainTitle

Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization

Details

Type

Article

Created At

23 Jan 2023