We present an unsupervised approach to recognizing discourse relations of CON-TRAST, EXPLANATION-EVIDENCE, CON-DITION 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.
An unsupervised approach was proposed to recognize discourse relations in REF , which extracts discourse relations that hold between arbitrary spans of text making use of cue phrases.
An Unsupervised Approach To Recognizing Discourse Relations
Annual Meeting Of The Association For Computational Linguistics
Computer Science