FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text

FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondeterministic finite-state automaton. There are five stages in the operation of FASTUS. In Stage 1, names and other fixed form expressions are recognized. In Stage 2, basic noun groups, verb groups, and prepositions and some other particles are recognized. In Stage 3, certain complex noun groups and verb groups are constructed. Patterns for events of interest are identified in Stage 4 and corresponding ``event structures'' are built. In Stage 5, distinct event structures that describe the same event are identified and merged, and these are used in generating database entries. This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures. FASTUS is very efficient and effective, and has been used successfully in a number of applications.

The Very Idea of Dynamic Semantics

"Natural languages are programming languages for minds." Can we or should we take this slogan seriously? If so, how? Can answers be found by looking at the various "dynamic" treatments of natural language developed over the last decade or so, mostly in response to problems associated with donkey anaphora? In Dynamic Logic of Programs, the meaning of a program is a binary relation on the set of states of some abstract machine. This relation is meant to model aspects of the effects of the execution of the program, in particular its input-output behavior. What, if anything, are the dynamic aspects of various proposed dynamic semantics for natural languages supposed to model? Is there anything dynamic to be modeled? If not, what is all the full about? We shall try to answer some, at least, of these questions and provide materials for answers to others.