Abstract:
This paper describes RESOLVE, a
system that uses decision trees to learn how to classify
coreferent phrases in the domain of business joint
ventures. An experiment is presented in which the
performance of RESOLVE is compareed to the performance of
a manually engineered set of rules for the same task. The
results show that decision trees achieve higher
performance than the rules in two of three evaluation
metrics developed for the coreference task. In addition to
achieving better performance than the rules, RESOLVE
provides a framework that facilitates the exploration of
the types of knowledge that are useful for solving the
coreference problem.
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