A parametric methodology for text classification
Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified do...
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| Veröffentlicht in: | Journal of information science Jg. 36; H. 4; S. 421 - 442 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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London, England
SAGE Publications
01.08.2010
Sage Publications Bowker-Saur Ltd |
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| ISSN: | 0165-5515, 1741-6485 |
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| Abstract | Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified document and all the pre-classified documents of each class and also calculate the similarity of the new document to the ‘average class document’ of each class. In both cases we use key phrases (text phrases or key terms) as the distinctive features of our text classification methodology and eventually the proposed text classification method is based on the automatic extraction of an authority list of key phrases that is appropriate for discriminating between different classes. In this paper, we apply this methodology in handling Greek text and we present the key concepts, the algorithms, and some critical decisions. A number of parameters of the mining algorithm are also fine tuned. The actual text classification system, the adopted (embedded) ideas and the alternative values of parameters are evaluated using two training sets (test collections). |
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| AbstractList | Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified document and all the pre-classified documents of each class and also calculate the similarity of the new document to the 'average class document' of each class. In both cases we use key phrases (text phrases or key terms) as the distinctive features of our text classification methodology and eventually the proposed text classification method is based on the automatic extraction of an authority list of key phrases that is appropriate for discriminating between different classes. In this paper, we apply this methodology in handling Greek text and we present the key concepts, the algorithms, and some critical decisions. A number of parameters of the mining algorithm are also fine tuned. The actual text classification system, the adopted (embedded) ideas and the alternative values of parameters are evaluated using two training sets (test collections). [PUBLICATION ABSTRACT] Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified document and all the pre-classified documents of each class and also calculate the similarity of the new document to the ‘average class document’ of each class. In both cases we use key phrases (text phrases or key terms) as the distinctive features of our text classification methodology and eventually the proposed text classification method is based on the automatic extraction of an authority list of key phrases that is appropriate for discriminating between different classes. In this paper, we apply this methodology in handling Greek text and we present the key concepts, the algorithms, and some critical decisions. A number of parameters of the mining algorithm are also fine tuned. The actual text classification system, the adopted (embedded) ideas and the alternative values of parameters are evaluated using two training sets (test collections). Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified document and all the pre-classified documents of each class and also calculate the similarity of the new document to the "average class document" of each class. In both cases we use key phrases (text phrases or key terms) as the distinctive features of our text classification methodology and eventually the proposed text classification method is based on the automatic extraction of an authority list of key phrases that is appropriate for discriminating between different classes. In this paper, we apply this methodology in handling Greek text and we present the key concepts, the algorithms, and some critical decisions. A number of parameters of the mining algorithm are also fine tuned. The actual text classification system, the adopted (embedded) ideas and the alternative values of parameters are evaluated using two training sets (test collections). [Reprinted by permission of Sage Publications, Ltd., copyright holder.] |
| Author | Skourlas, Christos Karanikolas, Nikitas N. |
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| Cites_doi | 10.1016/j.is.2008.05.002 10.1177/016555158801400104 10.3233/978-1-60750-934-9-444 10.1007/978-3-540-79490-5_15 10.1007/978-3-540-73499-4_63 10.1108/eb046814 10.1023/A:1025554732352 10.1108/EUM0000000007082 10.1007/s10791-006-9012-6 10.1109/TITB.2006.888705 10.2298/FUEE0603439K 10.1109/69.917564 10.1080/14639230050058310 10.2498/cit.1000759 10.1016/j.cmpb.2008.03.003 10.1108/eb047204 10.1002/asi.20648 |
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| Keywords | text indexing text classification key phrase extraction document management information retrieval Text mining Automatic classification Experimental result Similarity Information retrieval Information extraction Algorithm |
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| SubjectTerms | Algorithms Automatic classification Averages Categories Classification Collection Computerized information retrieval Content analysis Distinctive features Document management Exact sciences and technology Extraction Greek language Indexing. Classification. Abstracting Indexing. Classification. Abstracting. Syntheses Information and communication sciences Information and document structure and analysis Information processing Information processing and retrieval Information retrieval Information retrieval. Man machine relationship Information science. Documentation Mathematical analysis Methodology Natural language processing Phrases Research methodology Research process. Evaluation Sciences and techniques of general use Similarity Studies Text categorization Texts Training sets |
| Title | A parametric methodology for text classification |
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