Paper: Learning Features That Predict Cue Usage

ACL ID P97-1011
Title Learning Features That Predict Cue Usage
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997
Authors

Our goal is to identify the features that pre- dict the occurrence and placement of dis- course cues in tutorial explanations in or- der to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on in- tuition or small numbers of constructed ex- amples. We apply a machine learning pro- gram, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previ- ously thought to affect cue usage. Our ex- periments enable us to identify the features with most predictive power, and show that machine learning can be used to induce de- cision trees useful for text generation.