ML Anthology

A Lexical Based Semantic Bias for Theory Revision

Clifford Brunk, Michael J. Pazzani
ICML 1995 pp. 81-89
doi:10.1016/B978-1-55860-377-6.50019-0 /icml/1995/brunk1995icml-lexical/

Abstract

We present a new approach to theory revision that uses a lexically based semantics to help detect and correct errors in classification rules. The idea is that preferring lexically cohesive revisions will enhance the comprehensibility and ultimately the accuracy of rules. We explain how to associate terms in the rules with elements in a lexical class hierarchy and use distance within the hierarchy to estimate lexical cohesiveness. We evaluate the utility of this approach empirically using two relational domains.

ICML Semantic Scholar

Cite

Text

Brunk and Pazzani. "A Lexical Based Semantic Bias for Theory Revision." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50019-0

Markdown

[Brunk and Pazzani. "A Lexical Based Semantic Bias for Theory Revision." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/brunk1995icml-lexical/) doi:10.1016/B978-1-55860-377-6.50019-0

BibTeX

@inproceedings{brunk1995icml-lexical,
  title     = {{A Lexical Based Semantic Bias for Theory Revision}},
  author    = {Brunk, Clifford and Pazzani, Michael J.},
  booktitle = {International Conference on Machine Learning},
  year      = {1995},
  pages     = {81-89},
  doi       = {10.1016/B978-1-55860-377-6.50019-0},
  url       = {https://mlanthology.org/icml/1995/brunk1995icml-lexical/}
}