Evaluating Machine Learning for Information Extraction
neil ireson, fabio ciravegna, Marie Elaine Califf, Dayne Freytag, Nicholas Kushmerick and Alberto Lavelli
In: 22nd International Conference on Machine Learning (ICML 2005), August 7-11, 2005, Bonn, Germany.
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.