  Lexical selection in Machine Translation consists of several related
components. Two that have received a lot of attention are lexical mapping from
an underlying concept or lexical item, and choosing the correct
subcategorization frame based on argument structure. Because most MT
applications are small or relatively domain specific, a third component of
lexical selection is generally overlooked - distinguishing between lexical
items that are closely related conceptually. While some MT systems have
proposed using a 'world knowledge' module to decide which word is more
appropriate based on various pragmatic or stylistic constraints, we are
interested in seeing how much we can accomplish using a combination of syntax
and lexical semantics. By using separate ontologies for each language
implemented in FB-LTAGs, we are able to elegantly model the more specific and
language dependent syntactic and semantic distinctions necessary to further
filter the choice of the lexical item.
