Learning Features that Predict Cue Usage
cmp-lg
2007-05-23 v1 Computation and Language
Abstract
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.
Cite
@article{arxiv.cmp-lg/9710006,
title = {Learning Features that Predict Cue Usage},
author = {Barbara Di Eugenio and Johanna D. Moore and Massimo Paolucci},
journal= {arXiv preprint arXiv:cmp-lg/9710006},
year = {2007}
}
Comments
10 pages, 2 Postscript figures, uses aclap.sty, psfig.tex