Use of a genetic algorithm in brill's
transformation-based part-of-speech tagger
GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation, 2, page 2067--2073. Washington DC, USA, ACM Press, (25-29 June 2005)Abstract
The tagging problem in natural language processing is
to find a way to label every word in a text as a
particular part of speech, e.g., proper noun. An
effective way of solving this problem with high
accuracy is the transformation-based or "Brill"
tagger. In Brill's system, a number of transformation
templates are specified a priori that are instantiated
and ranked during a greedy search-based algorithm. This
paper describes a variant of Brill's implementation
that instead uses a genetic algorithm to generate the
instantiated rules and provide an adaptive ranking.
Based on tagging accuracy, the new system provides a
better hybrid evolutionary computation solution to the
part-of-speech (POS) problem than the previous attempt.
Although not able to make up for the use of a priori
knowledge used by Brill, the method appears to point
the way for an improved solution to the tagging
problem.
