Econo-ESA in semantic text similarity

Econo-ESA in semantic text similarity

Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter part. This part of the analysis multiplies a large matrix by a term vector to produce a high-dimensional concept vector. A similarity measurement between two texts is performed between two concept vectors...

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Main Authors: Rahutomo, Faisal, Aritsugi, Masayoshi
Format: Online
Language:English
Published: Springer International Publishing 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4003000/
id pubmed-4003000
recordtype oai_dc
spelling pubmed-40030002014-04-30 Econo-ESA in semantic text similarity Rahutomo, Faisal Aritsugi, Masayoshi Research Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter part. This part of the analysis multiplies a large matrix by a term vector to produce a high-dimensional concept vector. A similarity measurement between two texts is performed between two concept vectors with numerous dimensions. The cost is expensive in both interpretation and similarity measurement steps. This paper proposes an economic scheme of ESA, named econo-ESA. We investigate two aspects of this proposal: dimensional reduction and experiments with various data. We use eight recycling test collections in semantic text similarity. The experimental results show that both the dimensional reduction and test collection characteristics can influence the results. They also show that an appropriate concept reduction of econo-ESA can decrease the cost with minor differences in the results from the original ESA. Springer International Publishing 2014-03-19 /pmc/articles/PMC4003000/ /pubmed/24790807 http://dx.doi.org/10.1186/2193-1801-3-149 Text en © Rahutomo and Aritsugi; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Rahutomo, Faisal
Aritsugi, Masayoshi
spellingShingle Rahutomo, Faisal
Aritsugi, Masayoshi
Econo-ESA in semantic text similarity
author_facet Rahutomo, Faisal
Aritsugi, Masayoshi
author_sort Rahutomo, Faisal
title Econo-ESA in semantic text similarity
title_short Econo-ESA in semantic text similarity
title_full Econo-ESA in semantic text similarity
title_fullStr Econo-ESA in semantic text similarity
title_full_unstemmed Econo-ESA in semantic text similarity
title_sort econo-esa in semantic text similarity
description Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter part. This part of the analysis multiplies a large matrix by a term vector to produce a high-dimensional concept vector. A similarity measurement between two texts is performed between two concept vectors with numerous dimensions. The cost is expensive in both interpretation and similarity measurement steps. This paper proposes an economic scheme of ESA, named econo-ESA. We investigate two aspects of this proposal: dimensional reduction and experiments with various data. We use eight recycling test collections in semantic text similarity. The experimental results show that both the dimensional reduction and test collection characteristics can influence the results. They also show that an appropriate concept reduction of econo-ESA can decrease the cost with minor differences in the results from the original ESA.
publisher Springer International Publishing
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4003000/
_version_ 1612083196609953792

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