  We explore many ways of using conceptual distance measures in Word Sense
Disambiguation, starting with the Agirre-Rigau conceptual density measure. We
use a generalized form of this measure, introducing many (parameterized)
refinements and performing an exhaustive evaluation of all meaningful
combinations. We finally obtain a 42% improvement over the original algorithm,
and show that measures of conceptual distance are not worse indicators for
sense disambiguation than measures based on word-coocurrence (exemplified by
the Lesk algorithm). Our results, however, reinforce the idea that only a
combination of different sources of knowledge might eventually lead to accurate
word sense disambiguation.
