This paper presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in classifying relations over attribute-value learning paradigms such as Decision Trees, RIPPER...

This thesis is an inquiry into the nature of the high-level, rhetorical structure of unrestricted natural language texts, computational means to enable its derivation, and two applications (in automatic summarization and natural language generation) that follow from the ability to build such structures automatically. The thesis proposes a rst-order formalization of the high-level, rhetorical st...

Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rheto...

Using the RST annotated corpus (Carlson et al., 2003), we use simple statistics on the distribution of discourse markers or cue phrases as evidence of the three-way distinction of Contrast relations, Contrast, Antithesis and Concession, recognized in standard Rhetorical Structure Theory (RST, Mann & Thompson 1987). We also show that however, an intuitive marker of Contrast, is not actually used...

The notion that a text is coherent in virtue of the `relations' which hold between the elements of that text has become fairly common currency, both in the study of discourse coherence and in the eld of text generation. The set of relations proposed in Rhetorical Structure Theory (Mann and Thompson [14]) has had particular in uence in both of these elds. But the widespread adoption of `relation...

In this article we present the RST Spanish Treebank, the first corpus annotated with rhetorical relations for this language. We describe the characteristics of the corpus, the annotation criteria, the annotation procedure, the inter-annotator agreement, and other related aspects. Moreover, we show the interface that we have developed to carry out searches over the corpus’ annotated texts.