Text generation: using discourse strategies and focus constraints to generate natural language text
Text generation: using discourse strategies and focus constraints to generate natural language text
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
Explanatory power for medical expert systems: studies in the representation of causal relationships for clinical consultations
Generating natural language text in response to questions about database structure
Generating natural language text in response to questions about database structure
Discourse structures for text generation
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
The ROMPER system: responding to object-related misconceptions using perspective
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
Functional unification grammar revisited
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Explanation structures in XSEL
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Description strategies for naive and expert users
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Integrating qualitative reasoning and text planning to generate causal explanations
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Steps from explanation planning to model construction dialogues
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Qualifying Answers According to User Needs and Preferences
Fundamenta Informaticae
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A question answering system that provides access to a large amount of data will be most useful if it can tailor its answer to each user. In particular, a user's level of knowledge about the domain of discourse is an important factor in this tailoring. In previous work we determined that a user's level of domain knowledge affects the kind of information provided in an answer to a user's question as opposed to just the amount of information, as was previously proposed. We also explained how two distinct discourse strategies could be used to generate texts aimed at naive and expert users. Users are not necessarily truly expert or fully naive however, but can be anywhere along a knowledge spectrum whose extremes are naive and expert In this work, we show how our generation system, TAILOR, can use information about a user's level of expertise to combine several discourse strategies in a single text, choosing the most appropriate at each point in the generation process, in order to generate texts for users anywhere along the knowledge spectrum. TAILOR'S ability to combine discourse strategies based on a user model allows for the generation of a wider variety of texts and the most appropriate one for the user.