A Bayesian approach for understanding information-seeking queries

Explores a Bayesian approach for handling natural language queries for information access or retrieval. Natural language understanding is a key technology in human-computer interfaces, and often constitutes the front-end to information systems. In this work, we devised a Bayesian methodology for the identification of the user´s informational goals in information enquiries. We focus on a restricted domain, the air travel information systems (ATIS) domain, where a user´s informational goal often falls within a finite set of within-domain goals. We formulated the problem into N binary decisions, to allow for the identification of queries with multiple goals, as well as queries with out-of-domain goals. Experiments with the ATIS corpus shows that between 84.6% to 88.0% of the user queries are correctly handled via goal classification, rejection or multiple-goal identification