Using wavelet analysis for text categorization in digital libraries: a first experiment with Strathprints
Digital libraries increasingly benefit from research on automated text categorization for improved access. Such research is typically carried out by means of standard test collections. In this article, we present a pilot experiment of replacing such test collections by a set of 6,000 objects from a...
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| Published in | International journal on digital libraries Vol. 12; no. 1; pp. 3 - 12 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer-Verlag
01.07.2012
Springer Nature B.V |
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| Abstract | Digital libraries increasingly benefit from research on automated text categorization for improved access. Such research is typically carried out by means of standard test collections. In this article, we present a pilot experiment of replacing such test collections by a set of 6,000 objects from a real-world digital repository, indexed by Library of Congress Subject Headings, and test support vector machines in a supervised learning setting for their ability to reproduce the existing classification. To augment the standard approach, we introduce a combination of two novel elements: using functions for document content representation in Hilbert space, and adding extra semantics from lexical resources to the representation. Results suggest that wavelet-based kernels slightly outperformed traditional kernels on classification reconstruction from abstracts and vice versa from full-text documents, the latter outcome being due to word sense ambiguity. The practical implementation of our methodological framework enhances the analysis and representation of specific knowledge relevant to large-scale digital collections, in this case the thematic coverage of the collections. Representation of specific
knowledge
about digital collections is one of the basic elements of the persistent archives and the less studied one (compared to representations of digital objects and collections). Our research is an initial step in this direction developing further the methodological approach and demonstrating that text categorization can be applied to analyse the thematic coverage in digital repositories. |
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| AbstractList | Issue Title: Focused Issue on Persistent Archives Digital libraries increasingly benefit from research on automated text categorization for improved access. Such research is typically carried out by means of standard test collections. In this article, we present a pilot experiment of replacing such test collections by a set of 6,000 objects from a real-world digital repository, indexed by Library of Congress Subject Headings, and test support vector machines in a supervised learning setting for their ability to reproduce the existing classification. To augment the standard approach, we introduce a combination of two novel elements: using functions for document content representation in Hilbert space, and adding extra semantics from lexical resources to the representation. Results suggest that wavelet-based kernels slightly outperformed traditional kernels on classification reconstruction from abstracts and vice versa from full-text documents, the latter outcome being due to word sense ambiguity. The practical implementation of our methodological framework enhances the analysis and representation of specific knowledge relevant to large-scale digital collections, in this case the thematic coverage of the collections. Representation of specific knowledge about digital collections is one of the basic elements of the persistent archives and the less studied one (compared to representations of digital objects and collections). Our research is an initial step in this direction developing further the methodological approach and demonstrating that text categorization can be applied to analyse the thematic coverage in digital repositories.[PUBLICATION ABSTRACT] Digital libraries increasingly benefit from research on automated text categorization for improved access. Such research is typically carried out by means of standard test collections. In this article, we present a pilot experiment of replacing such test collections by a set of 6,000 objects from a real-world digital repository, indexed by Library of Congress Subject Headings, and test support vector machines in a supervised learning setting for their ability to reproduce the existing classification. To augment the standard approach, we introduce a combination of two novel elements: using functions for document content representation in Hilbert space, and adding extra semantics from lexical resources to the representation. Results suggest that wavelet-based kernels slightly outperformed traditional kernels on classification reconstruction from abstracts and vice versa from full-text documents, the latter outcome being due to word sense ambiguity. The practical implementation of our methodological framework enhances the analysis and representation of specific knowledge relevant to large-scale digital collections, in this case the thematic coverage of the collections. Representation of specific knowledge about digital collections is one of the basic elements of the persistent archives and the less studied one (compared to representations of digital objects and collections). Our research is an initial step in this direction developing further the methodological approach and demonstrating that text categorization can be applied to analyse the thematic coverage in digital repositories. Digital libraries increasingly benefit from research on automated text categorization for improved access. Such research is typically carried out by means of standard test collections. In this article, we present a pilot experiment of replacing such test collections by a set of 6,000 objects from a real-world digital repository, indexed by Library of Congress Subject Headings, and test support vector machines in a supervised learning setting for their ability to reproduce the existing classification. To augment the standard approach, we introduce a combination of two novel elements: using functions for document content representation in Hilbert space, and adding extra semantics from lexical resources to the representation. Results suggest that wavelet-based kernels slightly outperformed traditional kernels on classification reconstruction from abstracts and vice versa from full-text documents, the latter outcome being due to word sense ambiguity. The practical implementation of our methodological framework enhances the analysis and representation of specific knowledge relevant to large-scale digital collections, in this case the thematic coverage of the collections. Representation of specific knowledge about digital collections is one of the basic elements of the persistent archives and the less studied one (compared to representations of digital objects and collections). Our research is an initial step in this direction developing further the methodological approach and demonstrating that text categorization can be applied to analyse the thematic coverage in digital repositories. |
| Author | Darányi, Sándor Dobreva, Milena Wittek, Peter |
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| Keywords | Support vector machines Wavelet analysis Text categorization Digital libraries Analogical information representation Machine learning |
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