Our goal is to identify the features that predict cue selection and placement in order to devise strategies for automatic text generation. Much previous work in this area has relied on ad hoc methods. Our coding scheme for the exhaustive analysis of discourse allows a systematic evaluation and refinement of hypotheses concerning cues. We report two results based on this analysis: a comparison of the distribution of Sn~CE and BECAUSE in our corpus, and the impact of embeddedness on cue selection.
|Title of host publication||33rd Annual Meeting of the Association for Computational Linguistics, 26-30 June 1995, MIT, Cambridge, Massachusetts, USA, Proceedings.|
|Number of pages||6|
|Publication status||Published - 1995|