Comparing Approaches to Tag Discourse Relations

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.