  This paper presents a method to combine a set of unsupervised algorithms that
can accurately disambiguate word senses in a large, completely untagged corpus.
Although most of the techniques for word sense resolution have been presented
as stand-alone, it is our belief that full-fledged lexical ambiguity resolution
should combine several information sources and techniques. The set of
techniques have been applied in a combined way to disambiguate the genus terms
of two machine-readable dictionaries (MRD), enabling us to construct complete
taxonomies for Spanish and French. Tested accuracy is above 80% overall and 95%
for two-way ambiguous genus terms, showing that taxonomy building is not
limited to structured dictionaries such as LDOCE.
