Harald Lüngen , Alexander Mehler and Angelika Storrer Lexical - Semantic Resources in Automated Discourse Analysis

In this paper, we address the task of automatically determining which discourse relation holds between two text spans. We focus on relations that are not explicitly signalled by a discourse marker like but. While lexical models have been found useful for the task, they are also prone to data sparseness problems, which is a big drawback given the scarcity of discourse annotated data. We therefore investigate whether the use of lexical-semantic resources, such as WordNet, can be exploited to back-off to a more general representation of lexical information in cases were data are sparse. We compare such a semantic back-off strategy to morphological generalisations over word forms, such as stemming and lemmatising.