Domain and Language Independent Feature Extraction for Statistical Text Categorization
<|reference_start|>Domain and Language Independent Feature Extraction for Statistical Text Categorization: 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.<|reference_end|>
@article{bayer1996domain,
    title={Domain and Language Independent Feature Extraction for Statistical Text
  Categorization},
    author={Thomas Bayer, Ingrid Renz, Michael Stein, Ulrich Kressel (Daimler Benz
  Research and Technology)},
    journal={proceedings of workshop on language engineering for document
  analysis and recognition - ed. by L. Evett and T. Rose, part of the AISB 1996
  Workshop Series, April 96, Sussex University, England, 21-32 (ISBN 0 905
  488628)},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9607003},
    primaryClass={cmp-lg cs.CL}
}