Aligning Discourse and Argumentation Structures using Subtrees and Redescription Mining

Laurine Huber 1 Yannick Toussaint 2 Charlotte Roze 1 Mathilde Dargnat 3 Chloé Braud 1
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations. Contrary to previous works, we focus on comparing sub-structures and not only relation matches. Using data mining techniques , we show that discourse and argumen-tation most often align well, and the double annotation allows to derive a mapping between structures. Moreover, this approach enables the study of similarities between discourse structures and differences in their expressive power.
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Laurine Huber, Yannick Toussaint, Charlotte Roze, Mathilde Dargnat, Chloé Braud. Aligning Discourse and Argumentation Structures using Subtrees and Redescription Mining. 2019. ⟨hal-02165048⟩

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