Machine Learning of Language: A Model and a Problem
I will show how the memory-based approach, developed at the University of
Tilburg and the University of Antwerp, provides an elegant and efficient
model that fits natural language processing (NLP) tasks ranging from
low-level problems like part-of-speech tagging to high-level applications
like information extraction from text. Applications of this model lead to
competitive accuracies compared to alternative approaches, suggesting that
the proposed learning method has "the right bias" for NLP tasks. However,
in the second part of the talk, I will show that the superiority of one
learning approach over another for NLP tasks cannot be shown in a reliable
way using current methodology for comparative machine learning experiments.
I will go into the consequences of and possible patches for this
methodological problem which affects a large part of current research in
machine learning of language.
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Last modified: Fri Jun 14 18:26:50 MET DST 2002