Constrained decoding for text-level discourse parsing

Philippe Muller 1, * Stergos Afantenos 1 Pascal Denis 2 Nicholas Asher 1
* Auteur correspondant
2 MOSTRARE - Modeling Tree Structures, Machine Learning, and Information Extraction
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : This paper presents a novel approach to document-based discourse analysis by performing a global A* search over the space of possible structures while optimizing a global criterion over the set of potential coherence relations. Existing approaches to discourse analysis have so far relied on greedy search strategies or restricted themselves to sentence-level discourse parsing. Another advantage of our approach, over other global alternatives (like Maximum Spanning Tree decoding algorithms), is its flexibility in being able to integrate constraints (including linguistically motivated ones like the Right Frontier Constraint). Finally, our paper provides the first discourse parsing system for French; our evaluation is carried out on the Annodis corpus. While using a lot less training data than earlier approaches than previous work on English, our system manages to achieve state-of-the-art results, with F1-scores of 66.2 and 46.8 when compared to unlabeled and labeled reference structures.
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Communication dans un congrès
COLING - 24th International Conference on Computational Linguistics, Dec 2012, Mumbai, India. 2012
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Philippe Muller, Stergos Afantenos, Pascal Denis, Nicholas Asher. Constrained decoding for text-level discourse parsing. COLING - 24th International Conference on Computational Linguistics, Dec 2012, Mumbai, India. 2012. 〈hal-00750611〉

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