PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:1689
Deposited By:Fabio Ciravegna
Deposited On:28 November 2005