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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. | Learning Features that Predict Cue Usage Barbara Di Eugenio Johanna D. Moore Massimo Paolucci University of Pittsburgh Pittsburgh PA 15260 USA dieugeni jmoore paolucci cs.pitt.edu 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. 1 Introduction Discourse cues are words or phrases such as because first and although that mark structural and semantic relationships between discourse entities. They play a crucial role in many discourse processing tasks including plan recognition Litman and Allen 1987 text comprehension Cohen 1984 Hobbs 1985 Mann and Thompson 1986 Reichman-Adar 1984 and anaphora resolution Grosz and Sidner 1986 . Moreover research in reading comprehension indicates that felicitous use of cues improves comprehension and recall Goldman 1988 but that their indiscriminate use may have detrimental effects on recall Millis Graesser and Haberlandt 1993 . Our goal is to identify general strategies for cue usage that can be implemented for automatic text generation. From the generation perspective cue usage consists of three distinct but interrelated problems 1 occurrence- whether or not to include a cue in the generated text 2 placement where the cue should be placed in the text and 3 selection what lexical item s should be used. Prior work in text generation has focused on cue selection McKeown and Elhadad 1991 Elhadad and McKeown 1990 or on .
