Learning Features that Predict Cue Usage

Barbara Di Eugenio, Johanna D. Moore, Massimo Paolucci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, 7-12 July 1997, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
PublisherCornell University Press
Pages80-87
Number of pages8
Publication statusPublished - Jul 1997

Fingerprint

Dive into the research topics of 'Learning Features that Predict Cue Usage'. Together they form a unique fingerprint.

Cite this