Modeling the user's plans and goals
Computational Linguistics - Special issue on user modeling
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
The ROMPER system: responding to object-related misconceptions using perspective
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
A pragmatics-based approach to understanding intersentential ellipsis
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Ranking definitions with supervised learning methods
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A supervised learning approach to search of definitions
Journal of Computer Science and Technology - Special section on China AVS standard
Hi-index | 0.00 |
Definitions may be made up of one or more components, which correspond to strategic predicates. The selection of which components to use in giving a definition in a task-oriented dialogue depends heavily on the needs of the user. The selection strategy we present involves weighting possible strategic predicates and the propositions used to fill them at multiple points throughout an ongoing dialogue and at the actual time of giving the definition. Weighting will be influenced by a model of the user's domain knowledge, task-related plans and goals, and receptivity to the different kinds of information that could be presented. An utterance can then be produced that incorporates the most important information while adhering to common rhetorical practices.