Tailoring the Content of Dynamically Generated Explanations
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This paper describes briefly an approach for tailoring the content of automatically generated hypertext explanations. The implemented pilot system hylite+ has a dynamically updated user model, which is used by the language generation modules to adapt the explanations to the user beliefs and preferences. The goal is to provide sufficiently detailed information which, on one hand helps the user by explaining the unknown terms, and on the other, avoids repeating already known facts.
KeywordsNatural Language Generation Unknown Term Relevant Proposition Tape Drive Incorrect Belief
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