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ACL
1997

Learning Features that Predict Cue Usage

13 years 5 months ago
Learning Features that Predict Cue Usage
Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. We apply a machine learning program, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. Our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.
Barbara Di Eugenio, Johanna D. Moore, Massimo Paol
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where ACL
Authors Barbara Di Eugenio, Johanna D. Moore, Massimo Paolucci
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