Abstract
Language Understanding in limited domains is here approached as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process. Furthermore, these models are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-step learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the understanding of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command at.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
