  This paper presents a statistical decision procedure for lexical ambiguity
resolution. The algorithm exploits both local syntactic patterns and more
distant collocational evidence, generating an efficient, effective, and highly
perspicuous recipe for resolving a given ambiguity. By identifying and
utilizing only the single best disambiguating evidence in a target context, the
algorithm avoids the problematic complex modeling of statistical dependencies.
Although directly applicable to a wide class of ambiguities, the algorithm is
described and evaluated in a realistic case study, the problem of restoring
missing accents in Spanish and French text.
