Attention, intentions, and the structure of discourse
Computational Linguistics
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
Discourse segmentation by human and automated means
Computational Linguistics
Discourse relations: a structural and presuppositional account using lexicalised TAG
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
| Hi-index | 0.01 |
Like speakers of any natural language, speakers of English potentially have many different word orders in which to encode a single meaning. One key factor in speakers' use of certain non-canonical word orders in English is their ability to contribute information about syntactic and semantic discourse relations. Explicit annotation of discourse relations is a difficult and subjective task. In order to measure the correlations between different word orders and various discourse relations, this project utilizes a model in which discourse relations are approximated using a set of lower-level linguistic features, which are more easily and reliably annotated than discourse relations themselves. The featural model provides statistical evidence for the claim that speakers use non-canonicals to communicate information about discourse structure.