ML Anthology

Evaluating Machine Learning for Information Extraction

Neil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli
ICML 2005 pp. 345-352
doi:10.1145/1102351.1102395 /icml/2005/ireson2005icml-evaluating/

Abstract

Comparative evaluation of Machine Learning (ML) systems used for Information Extraction (IE) has suffered from various inconsistencies in experimental procedures. This paper reports on the results of the Pascal Challenge on Evaluating Machine Learning for Information Extraction, which provides a standardised corpus, set of tasks, and evaluation methodology. The challenge is described and the systems submitted by the ten participants are briefly introduced and their performance is analysed.

PDF ICML Semantic Scholar

Cite

Text

Ireson et al. "Evaluating Machine Learning for Information Extraction." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102395

Markdown

[Ireson et al. "Evaluating Machine Learning for Information Extraction." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/ireson2005icml-evaluating/) doi:10.1145/1102351.1102395

BibTeX

@inproceedings{ireson2005icml-evaluating,
  title     = {{Evaluating Machine Learning for Information Extraction}},
  author    = {Ireson, Neil and Ciravegna, Fabio and Califf, Mary Elaine and Freitag, Dayne and Kushmerick, Nicholas and Lavelli, Alberto},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {345-352},
  doi       = {10.1145/1102351.1102395},
  url       = {https://mlanthology.org/icml/2005/ireson2005icml-evaluating/}
}