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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.
AbstractOn 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. [Edit]
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