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Knowledge Extraction with Non-Negative Matrix Factorization for Text Classification

2009-09Book part Open access
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
dc.contributor.authorSilva, Catarina
dc.contributor.authorRibeiro, Bernardete
dc.date.accessioned2025-06-06T11:49:06Z
dc.date.available2025-06-06T11:49:06Z
dc.date.issued2009-09
dc.description10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, 23 September 2009 through 26 September 2009 - Code 79260
dc.description.abstractText classification has received increasing interest over the past decades for its wide range of applications driven by the ubiquity of textual information. The high dimensionality of those applications led to pervasive use of dimensionality reduction methods, often black-box feature extraction non-linear techniques. We show how Non-Negative Matrix Factorization (NMF), an algorithm able to learn a parts-based representation of data by imposing non-negativity constraints, can be used to represent and extract knowledge from a text classification problem. The resulting reduced set of features is tested with kernel-based machines on Reuters-21578 benchmark showing the method's performance competitiveness.eng
dc.identifier.citationSilva, C., Ribeiro, B. (2009). Knowledge Extraction with Non-Negative Matrix Factorization for Text Classification. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_37.
dc.identifier.doi10.1007/978-3-642-04394-9_37
dc.identifier.eissn1611-3349
dc.identifier.isbn9783642043932
dc.identifier.isbn9783642043949
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10400.8/13155
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-642-04394-9_37
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofIntelligent Data Engineering and Automated Learning - IDEAL 2009
dc.rights.uriN/A
dc.subjectSupport Vector Machine
dc.subjectSemantic Feature
dc.subjectNonnegative Matrix Factorization
dc.subjectPositive Matrix Factorization
dc.subjectKnowledge Extraction
dc.titleKnowledge Extraction with Non-Negative Matrix Factorization for Text Classificationeng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage308
oaire.citation.startPage300
oaire.citation.titleLecture Notes in Computer Science
oaire.citation.volume5788
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSilva
person.givenNameCatarina
person.identifier.orcid0000-0002-5656-0061
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublication.latestForDiscoveryee28e079-5ca7-4842-9094-372c40f75c38

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Text classification has received increasing interest over the past decades for its wide range of applications driven by the ubiquity of textual information. The high dimensionality of those applications led to pervasive use of dimensionality reduction methods, often black-box feature extraction non-linear techniques. We show how Non-Negative Matrix Factorization (NMF), an algorithm able to learn a parts-based representation of data by imposing non-negativity constraints, can be used to represent and extract knowledge from a text classification problem. The resulting reduced set of features is tested with kernel-based machines on Reuters-21578 benchmark showing the method's performance competitiveness.
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