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Keyword Extraction using Semantic Analysis

by Mohamed H. Haggag
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 1
Year of Publication: 2013
Authors: Mohamed H. Haggag
10.5120/9889-4445

Mohamed H. Haggag . Keyword Extraction using Semantic Analysis. International Journal of Computer Applications. 61, 1 ( January 2013), 1-6. DOI=10.5120/9889-4445

@article{ 10.5120/9889-4445,
author = { Mohamed H. Haggag },
title = { Keyword Extraction using Semantic Analysis },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 1 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number1/9889-4445/ },
doi = { 10.5120/9889-4445 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:07:51.874144+05:30
%A Mohamed H. Haggag
%T Keyword Extraction using Semantic Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 1
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Keywords are list of significant words or terms that best present the document context in brief and relate to the textual context. Extraction models are categorized into either statistical, linguistic, machine learning or a combination of these approaches. This paper introduces a model for extracting keywords based on their relatedness weight among the entire text terms. Strength of terms relationship is evaluated by semantic similarity. Document terms are assigned a weighted metric based on the likeness of their meaning content. Terms that are strongly co-related to each other are highly considered in individual terms semantic similarity. Provision of the overall terms similarity is crucial for defining relevant keywords that most expressing the text in both frequency and weighted likeness. Keywords are recursively evaluated according to their cohesion to each other and to the document context. The proposed model showed enhanced precision and recall extraction values over other approaches.

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Index Terms

Computer Science
Information Sciences

Keywords

Keywords Extraction Sematic Similarity Semantic Relatedness Semantic Analysis Word Sense

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