Computer Science – Computation and Language
Scientific paper
1996-07-02
proceedings of workshop on language engineering for document analysis and recognition - ed. by L. Evett and T. Rose, part of t
Computer Science
Computation and Language
12 pages, TeX file, 9 Postscript figures, uses epsf.sty
Scientific paper
A generic system for text categorization is presented which uses a representative text corpus to adapt the processing steps: feature extraction, dimension reduction, and classification. Feature extraction automatically learns features from the corpus by reducing actual word forms using statistical information of the corpus and general linguistic knowledge. The dimension of feature vector is then reduced by linear transformation keeping the essential information. The classification principle is a minimum least square approach based on polynomials. The described system can be readily adapted to new domains or new languages. In application, the system is reliable, fast, and processes completely automatically. It is shown that the text categorizer works successfully both on text generated by document image analysis - DIA and on ground truth data.
Bayer Thomas
Kressel Ulrich
Renz Ingrid
Stein Michael
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