Year: 1996
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Bayer, Thomas, Renz, Ingrid, Stein, Michael, Kressel, Ulrich
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1676530625