AOC-Poset on discourse and argumentation subgraphs: what can we learn on their dependencies?
| Autor: | Huber, Laurine, Reynaud, Justine, Dargnat, Mathilde, Toussaint, Yannick |
|---|---|
| Přispěvatelé: | Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Analyse et Traitement Informatique de la Langue Française (ATILF), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), IMPACT-OLKi, ANR-15-IDEX-0004,LUE,Isite LUE(2015) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematics::Combinatorics
Computer Science::Discrete Mathematics Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] MathematicsofComputing_DISCRETEMATHEMATICS [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | Concept Lattice and their Applications Concept Lattice and their Applications, Jun 2020, Tallinn, Estonia |
| Popis: | International audience; We aim at finding and understanding dependencies between linguistic structures which differ in terms of constraints and expressive power. It has been shown that studying dependencies between the argu-mentation structure (ARG) and the Rhetorical Structure Theory (RST) is non-trivial and requires a fine methodology. In this paper, we propose to take advantage of the AOC-Poset structure to understand how the subgraphs alignements occur in a small corpus annotated in ARG and RST. We formalize the structures as graphs from which we extract both subgraphs and subgraphs alignments, matching those subgraphs which include the same text segments. Based on these extractions, we build a formal context where the objects are the texts and the attributes are the subgraphs and the subgraphs alignments. We show what we can learn from the dependencies between the structures by mining the AOC-Poset made of these attributes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|