  In this paper, we discuss the utility and deficiencies of existing ontology
resources for a number of language processing applications. We describe a
technique for increasing the semantic type coverage of a specific ontology, the
National Library of Medicine's UMLS, with the use of robust finite state
methods used in conjunction with large-scale corpus analytics of the domain
corpus. We call this technique "semantic rerendering" of the ontology. This
research has been done in the context of Medstract, a joint Brandeis-Tufts
effort aimed at developing tools for analyzing biomedical language (i.e.,
Medline), as well as creating targeted databases of bio-entities, biological
relations, and pathway data for biological researchers. Motivating the current
research is the need to have robust and reliable semantic typing of syntactic
elements in the Medline corpus, in order to improve the overall performance of
the information extraction applications mentioned above.
