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
Hauptverfasser: Karanikolas, Nikitas N., Skourlas, Christos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.08.2010
Sage Publications
Bowker-Saur Ltd
Schlagworte:
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
text indexing
text classification
key phrase extraction
document management
information retrieval
Text mining
Automatic classification
Experimental result
Similarity
Information retrieval
Information extraction
Algorithm
ISSN:0165-5515, 1741-6485
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Beschreibung
Zusammenfassung: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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ISSN:0165-5515
1741-6485
DOI:10.1177/0165551510368620