Seminar on Computational Learning and
Adaptation
Automatic Acquisition of Pronunciation Rules Using Error-Driven Learning
Raymond Flournoy
Amikai, Inc
San Francisco, CA
ray.flournoy@amikai.com
The development of error-driven learning, also known as
transformation-based learning or Brill's algorithm, was an important
landmark in part-of-speech (POS) tagging research, because it showed
that very good tagging results could be attained with simple training
and almost no human intervention. Its results were equivalent to much
more labor-intensive systems which were based on human-encoded rules
and grammars. In this work, I show how error-driven learning can be
adapted and generalized to apply to grapheme-phoneme conversion (GPC),
the task of determining the pronunciation of written words. After
describing the fundamental differences between POS tagging and GPC
which keep us from applying error-driven learning directly, I describe
how to adapt the approach to handle these differences and I then give
some experimental results. In addition, I discuss how a number of
other tasks within Natural Language Processing can also be handled by
this generalized form of error-driven learning.
Date: Thurs., Jan 25
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Time: 4:15-5:30PM
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Place: Cordura 100
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