PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Comparing Learning Approaches to Coreference Resolution. There is More to it Than 'Bias'.
Véronique Hoste and Walter Daelemans
In: Workshop on Meta-Learning, Aug 2005, Bonn, Germany.

Abstract

On the basis of results on three coreference resolution data sets we show that when following current practice in comparing learning methods, we cannot reliably conclude much about their suitability for a given task. In an empirical study of the behavior of representatives of two machine learning paradigms, viz. lazy learning and rule induction on the task of coreference resolution we show that the initial di erences between learning techniques are easily overruled when taking into account factors such as feature selection, algorithm parameter optimization, sample selection and their interaction. We propose genetic algorithms as an elegant method to overcome this costly optimization.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:1387
Deposited By:Walter Daelemans
Deposited On:28 November 2005