Discourse strategies for generating natural-language text
Artificial Intelligence
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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It is widely accepted that in a text, sentences and clauses cannot be understood in isolation but in relation with each other through discourse relations that may or may not be explicitly marked. Discourse relations have been found useful in many applications such as machine translation, text summarization, and question answering; however, they are often not considered in computational language applications because domain and genre independent robust discourse parsers are very few. In this paper, we analyze existing approaches to identify five discourse relations automatically (namely, comparison, contingency, illustration, attribution, and topic-opinion), and propose a new approach to identify attributive relations. We evaluate the accuracy of each approach with respect to the discourse relations it can identify and compare it to a human gold standard. The evaluation results show that the state of the art systems are rather effective at identifying most of the relations considered, but other relations such as attribution are still not identified with high accuracy.