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. 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) Souza, 1990; Vander Linden and Martin, 1995) . Other hypotheses about cue usage derive from work on discourse coherence and structure. Previous research (Hobbs, 1985; Grosz and Sidner, 1986; Schiffrin, 1987; Mann and Thompson, 1988; Elhadad and McKeown, 1990) , which has been largely descriptive, suggests factors such as structural features of the discourse (e.g., level of embedding and segment complexity), intentional and informational relations in that structure, ordering of relata, and syntactic form of discourse constituents. Moser and Moore (1995; 1997) coded a corpus of naturally occurring tutorial explanations for the range of features identified in prior work. Because they were also interested in the contrast between occurrence and non-occurrence of cues, they exhaustively coded for all of the factors thought to contribute to cue usage in all of the text. From their study, Moscr and Moore identified several interesting correlations between particular features and specific aspects of cue usage, and were able to test specific hypotheses from the hterature that were based on constructed examples. In this paper, we focus on cue occurrence and placement, and present an empirical study of the hypotheses provided by previous research, which have never been systematically evaluated with naturally occurring data. Wc use a machine learning program, C4.5 (Quinlan, 1993), on the tagged corpus of Moser and Moore to induce decision trees. The number of coded features and their interactions makes the manual construction of rules that predict cue occurrence and placement an intractable task. Our results largely confirm the suggestions from the hterature, and clarify them by highhghting the most influential features for a particular task. Discourse structure, in terms of both segment structure and levels of embedding, affects cue occurrence the most; intentional relations also play an important role. For cue placement, the most important factors are syntactic structure and segment complexity. The paper is organized as follows. In Section 2 we discuss previous research in more detail. Section 3 provides an overview of Moser and Moore's coding scheme. In Section 4 we present our learning experiments, and in Section 5 we discuss our results and conclude. 80
REF used C4.5 to predict cue occurrence and placement.
Learning Features That Predict Cue Usage
Annual Meeting Of The Association For Computational Linguistics
Computer Science