A novel system for extracting information from stereotyped voice traffic is described. Off-the-air recordings of commercial air traffic control communications are interpreted in order to identify the flights present and determine the scenario (e.g., takeoff, landing) that they are following. The system combines algorithms from signal segmentation, speaker segregation, speech recognition, natural language parsing, and topic classification into a single system. Initial evaluation of the algorithm on data recorded at Dallas-Fort Worth airport yields performance of 68% detection of flights with 98% precision at an operating point where 76% of the flight identifications are correctly recognized. In tower recording containing both takeoff and landing scenarios, flights are correctly classified as takeoff or landing 94% of the time.>

This paper addresses issues that arose in applying the model for discourse entity (DE) generation in B. Webber's work (1978, 1983) to an interactive multimodal interface. Her treatment was extended in 4 areas: (1) the notion of context dependence of DEs was formalized in an intensional logic, (2) the treatment of DEs for indefinite NPs was modified to use skolem functions, (3) the treatment of dependent quantifiers was generalized, and (4) DEs originating from non-linguistic sources, such as pointing actions, were taken into account. The discourse entities are used in intra- and extra-sentential pronoun resolution in BBN Janus.

An improved version of IRACQ (for Interpretation Rule ACQuisition) is presented.I Our approach to semantic knowledge acquisition: 1 ) is in the context of a general purpose NL interface rather than one that accesses only databases, 2) employs a knowledge representation formalism with limited inferencing capabilities, 3) assumes a trained person but not an AI expert, and 4) provides a complete environment for not only acquiring semantic knowledge, but also maintaining and editing it in a consistent knowledge base.IRACQ is currently in use at the Naval Ocean Systems Center.