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Abstract:
Recently, a model for supervised learning of probabilistic
transducers represented by suffix trees was introduced. However,
this algorithm tends to build very large trees, requiring very
large amounts of computer memory. In this paper, we propose a new,
more compact, transducer model in which one shares the parameters
of distributions associated to contexts yielding similar
conditional output distributions. We illustrate the advantages of
the proposed algorithm with comparative experiments on inducing a
noun phrase recognizer.
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