Local and Global Latent Semantic Analysis for Text Categorization
In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted o...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ghanem, Khadoudja [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Umfang: |
1 Online-Ressource |
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| Übergeordnetes Werk: |
Enthalten in: International journal of information retrieval research - Hershey, Pa : IGI Global, 2011, 4(2014), 3, Seite 1-13 |
|---|---|
| Übergeordnetes Werk: |
volume:4 ; year:2014 ; number:3 ; pages:1-13 |
| Links: |
|---|
| DOI / URN: |
10.4018/IJIRR.2014070101 |
|---|
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NLEJ251811611 |
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| 520 | |a In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved | ||
| 653 | |a Class Representative Term Vector |a Clustering |a Latent Semantic Analysis |a Text Classification | ||
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10.4018/IJIRR.2014070101 doi (DE-627)NLEJ251811611 (VZGNL)10.4018/IJIRR.2014070101 DE-627 ger DE-627 rakwb eng Ghanem, Khadoudja verfasserin aut Local and Global Latent Semantic Analysis for Text Categorization 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved Class Representative Term Vector Clustering Latent Semantic Analysis Text Classification Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 4(2014), 3, Seite 1-13 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:4 year:2014 number:3 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 4 2014 3 1-13 |
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10.4018/IJIRR.2014070101 doi (DE-627)NLEJ251811611 (VZGNL)10.4018/IJIRR.2014070101 DE-627 ger DE-627 rakwb eng Ghanem, Khadoudja verfasserin aut Local and Global Latent Semantic Analysis for Text Categorization 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved Class Representative Term Vector Clustering Latent Semantic Analysis Text Classification Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 4(2014), 3, Seite 1-13 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:4 year:2014 number:3 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 4 2014 3 1-13 |
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10.4018/IJIRR.2014070101 doi (DE-627)NLEJ251811611 (VZGNL)10.4018/IJIRR.2014070101 DE-627 ger DE-627 rakwb eng Ghanem, Khadoudja verfasserin aut Local and Global Latent Semantic Analysis for Text Categorization 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved Class Representative Term Vector Clustering Latent Semantic Analysis Text Classification Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 4(2014), 3, Seite 1-13 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:4 year:2014 number:3 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 4 2014 3 1-13 |
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10.4018/IJIRR.2014070101 doi (DE-627)NLEJ251811611 (VZGNL)10.4018/IJIRR.2014070101 DE-627 ger DE-627 rakwb eng Ghanem, Khadoudja verfasserin aut Local and Global Latent Semantic Analysis for Text Categorization 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved Class Representative Term Vector Clustering Latent Semantic Analysis Text Classification Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 4(2014), 3, Seite 1-13 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:4 year:2014 number:3 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 4 2014 3 1-13 |
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10.4018/IJIRR.2014070101 doi (DE-627)NLEJ251811611 (VZGNL)10.4018/IJIRR.2014070101 DE-627 ger DE-627 rakwb eng Ghanem, Khadoudja verfasserin aut Local and Global Latent Semantic Analysis for Text Categorization 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved Class Representative Term Vector Clustering Latent Semantic Analysis Text Classification Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 4(2014), 3, Seite 1-13 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:4 year:2014 number:3 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 4 2014 3 1-13 |
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In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved |
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In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved |
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In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251811611</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205143925.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/IJIRR.2014070101</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251811611</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/IJIRR.2014070101</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ghanem, Khadoudja</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Local and Global Latent Semantic Analysis for Text Categorization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Class Representative Term Vector</subfield><subfield code="a">Clustering</subfield><subfield code="a">Latent Semantic Analysis</subfield><subfield code="a">Text Classification</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of information retrieval research</subfield><subfield code="d">Hershey, Pa : IGI Global, 2011</subfield><subfield code="g">4(2014), 3, Seite 1-13</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244419159</subfield><subfield code="w">(DE-600)2703390-9</subfield><subfield code="x">2155-6385</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:1-13</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2014070101&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2014</subfield><subfield code="e">3</subfield><subfield code="h">1-13</subfield></datafield></record></collection>
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