Inducing Discourse Connectives from Parallel Texts

Discourse connectives (e.g. however, because) are terms that explicitly express discourse relations in a coherent text. While a list of discourse connectives is useful for both theoretical and empirical research on discourse relations, few languages currently possess such a resource. In this article, we propose a new method that exploits parallel corpora and collocation extraction techniques to automatically induce discourse connectives. Our approach is based on identifying candidates and ranking them using Log-Likelihood Ratio. Then, it relies on several filters to filter the list of candidates, namely: Word-Alignment, POS patterns, and Syntax. Our experiment to induce French discourse connectives from an English-French parallel text shows that Syntactic filter achieves a much higher MAP value (0.39) than the other filters, when compared with LEXCONN resource.