An ACG View on G-TAG and Its g-Derivation

Laurence Danlos 1 Aleksandre Maskharashvili 2 Sylvain Pogodalla 2
1 ALPAGE - Analyse Linguistique Profonde à Grande Echelle ; Large-scale deep linguistic processing
Inria Paris-Rocquencourt, UPD7 - Université Paris Diderot - Paris 7
2 SEMAGRAMME - Semantic Analysis of Natural Language
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : G-TAG is a Tree Adjoining Grammar (TAG) based formalism which was specifically designed for the task of text generation. Contrary to TAG, the derivation structure becomes primary, as pivot between the conceptual representation and the surface form. This is a shared feature with the encoding of TAG into Abstract Categorial Grammars. This paper propose to study G-TAG from an ACG perspective. We rely on the reversibility property of ACG that makes both parsing and generation fall within a common morphism inversion process. Doing so, we show how to overcome some limitations of G-TAG regarding predicative adjunction and how to propose alternative approaches to some phenomena.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [15 references]  Display  Hide  Download
https://hal.inria.fr/hal-00999633
Contributor : Sylvain Pogodalla <>
Submitted on : Tuesday, June 3, 2014 - 5:36:45 PM
Last modification on : Friday, January 4, 2019 - 5:33:24 PM
Document(s) archivé(s) le : Wednesday, September 3, 2014 - 1:00:48 PM

File

LacL2014.pdf
Publisher files allowed on an open archive

Identifiers

Collections

UNIV-PARIS7 | INRIA | UNIV-LORRAINE | LORIA-NLPKD | LORIA | USPC | INRIA-ROCQ

Citation

Laurence Danlos, Aleksandre Maskharashvili, Sylvain Pogodalla. An ACG View on G-TAG and Its g-Derivation. Logical Aspects of Computational Linguistics: 8th International Conference, LACL 2014, Toulouse, France, June 18-20, 2014. Proceedings, Jun 2014, Toulouse, France. pp.70-82, ⟨10.1007/978-3-662-43742-1_6⟩. ⟨hal-00999633⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

427

Files downloads

284

Contact


archive-ouverte@inria.fr