  Two classes of methods have been shown to be useful for resolving lexical
ambiguity. The first relies on the presence of particular words within some
distance of the ambiguous target word; the second uses the pattern of words and
part-of-speech tags around the target word. These methods have complementary
coverage: the former captures the lexical ``atmosphere'' (discourse topic,
tense, etc.), while the latter captures local syntax. Yarowsky has exploited
this complementarity by combining the two methods using decision lists. The
idea is to pool the evidence provided by the component methods, and to then
solve a target problem by applying the single strongest piece of evidence,
whatever type it happens to be. This paper takes Yarowsky's work as a starting
point, applying decision lists to the problem of context-sensitive spelling
correction. Decision lists are found, by and large, to outperform either
component method. However, it is found that further improvements can be
obtained by taking into account not just the single strongest piece of
evidence, but ALL the available evidence. A new hybrid method, based on
Bayesian classifiers, is presented for doing this, and its performance
improvements are demonstrated.
