  Resolution of lexical ambiguity, commonly termed ``word sense
disambiguation'', is expected to improve the analytical accuracy for tasks
which are sensitive to lexical semantics. Such tasks include machine
translation, information retrieval, parsing, natural language understanding and
lexicography. Reflecting the growth in utilization of machine readable texts,
word sense disambiguation techniques have been explored variously in the
context of corpus-based approaches. Within one corpus-based framework, that is
the similarity-based method, systems use a database, in which example sentences
are manually annotated with correct word senses. Given an input, systems search
the database for the most similar example to the input. The lexical ambiguity
of a word contained in the input is resolved by selecting the sense annotation
of the retrieved example. In this research, we apply this method of resolution
of verbal polysemy, in which the similarity between two examples is computed as
the weighted average of the similarity between complements governed by a target
polysemous verb. We explore similarity-based verb sense disambiguation focusing
on the following three methods. First, we propose a weighting schema for each
verb complement in the similarity computation. Second, in similarity-based
techniques, the overhead for manual supervision and searching the large-sized
database can be prohibitive. To resolve this problem, we propose a method to
select a small number of effective examples, for system usage. Finally, the
efficiency of our system is highly dependent on the similarity computation
used. To maximize efficiency, we propose a method which integrates the
advantages of previous methods for similarity computation.
