SGML-Based Markup as a Step toward Improving Knowledge Acquisition for Text Generation

Reva Freedman, Yujian Zhou, Jung Hee Kim, Michael Glass, and Martha Evens

We are investigating computer-assisted methods for identifying plan operators at both the conversational strategy and surface generation levels. We are using standard-conforming SGML markup on our corpus in order to be able to process it mechanically. We are using C4.5 to identify rules of the form "when is goal x implemented with plan y?". We are currently testing these methods in the knowledge acquisition process for the text generation component of C1RCSIM-Tutor v. 3, a natural-language based intelligent tutoring system.


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