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About:
There are various ways of measuring how "typical" or representative an
instance is with respect to its class. Previous work has shown that
when machine learning is applied to many natural language processing
tasks, non-typical training examples play an important role in
improving generalization accuracy. We are exploring whether such
results generalize to several
classification tasks in the area of spoken dialogue. In addition,
we are exploring how different formalizatons of "typicality" impact
the performance of memory-based and rule-based learning algorithms.
People:
Publications:
Related Publications:
- Previous publications on the spoken dialogue corpus currently being examined
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