Text Categorization and Relational Learning

Cohen, William W.

doi:10.1016/B978-1-55860-377-6.50024-4

Text Categorization and Relational Learning

William W. Cohen

ICML 1995 pp. 124-132

doi:10.1016/B978-1-55860-377-6.50024-4 /icml/1995/cohen1995icml-text/

Abstract

We evaluate the first order learning system FOIL on a series of text categorization problems. It is shown that FOIL usually forms classifiers with lower error rates and higher rates of precision and recall with a relational encoding than with a propositional encoding. We show that FOIL's performance can be improved by relation selection, a first order analog of feature selection. Relation selection improves FOIL's performance as measured by any of recall, precision, F-measure, or error rate. With an appropriate level of relation selection, FOIL appears to be competitive with or superior to existing propositional techniques.

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Cite

Text

Cohen. "Text Categorization and Relational Learning." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50024-4

Markdown

[Cohen. "Text Categorization and Relational Learning." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/cohen1995icml-text/) doi:10.1016/B978-1-55860-377-6.50024-4

BibTeX

@inproceedings{cohen1995icml-text,
  title     = {{Text Categorization and Relational Learning}},
  author    = {Cohen, William W.},
  booktitle = {International Conference on Machine Learning},
  year      = {1995},
  pages     = {124-132},
  doi       = {10.1016/B978-1-55860-377-6.50024-4},
  url       = {https://mlanthology.org/icml/1995/cohen1995icml-text/}
}