Abstract
We present an unsupervised approach to recognizing discourse relations of CONTRAST, EXPLANATION-EVIDENCE, CONDITION and ELABORATION that hold between arbitrary spans of texts. We show that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases.
Cite
CITATION STYLE
Marcu, D., & Echihabi, A. (2002). An unsupervised approach to recognizing discourse relations. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 2002-July, pp. 368–375). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1073083.1073145
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
