Evaluating machine learning for information extraction

Authors:
Neil Ireson;Fabio Ciravegna;Mary Elaine Califf;Dayne Freitag;Nicholas Kushmerick;Alberto Lavelli
Affiliations:
University of Sheffield, Sheffield, UK;University of Sheffield, Sheffield, UK;Illinois State University, Normal, IL;Fair Isaac Corporation, Minneapolis, MN;University College Dublin, Ireland;Centro per la Ricerca Scientifica e Tecnologica, Povo TN, Italy
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

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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.