SemRep is a natural language processing system designed to recover semantic propositions from biomedical text using underspecified syntactic analysis and structured domain knowledge. A large syntactic lexicon of general and medical English and a stochastic tagger support a partial categorial analysis that identifies simple noun phrases and verb groups. Domain knowledge is provided by components of the Unified Medical Language System (UMLS). The Metathesaurus contains biomedical concepts categorized into semantic classes (or types) that serve as arguments of semantic predications stipulated in the Semantic Network.
During the process of interpretation, simple noun phrases functioning as referring expressions are mapped to concepts in the Metathesaurus, while syntactic phenomena that "indicate" semantic predicates, including verbs, prepositions, nominalizations, and the head-modifier relation in noun phrases, are mapped to predicates in the Semantic Network. Syntactic constraints on argument identification are controlled by statements expressed in a type of dependency grammar and at least partially address argument coordination, relativization, and negation. Domain restrictions are enforced by a meta-rule that ensures that all semantic propositions identified by SemRep are sanctioned by a predication in the Semantic Network.
SemRep serves as the basis for several ongoing research initiatives in biomedical information management, including efforts directed at extracting medical and molecular biology information from text, processing clinical data in patient records, and providing highly accurate information-retrieval services to physicians at the point of care.
For the colloquium series schedule, see the UMD Computational Linguistics Colloquium Series web page at http://umiacs.umd.edu/~resnik/cl_colloquium/. If you are interested in meeting with the speaker, please contact Philip Resnik (resnik@umiacs.umd.edu).