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Deductive Parsing with an Unbounded Type Lexicon

Konstantinos Kogkalidis 1 Michael Moortgat 1 Richard Moot 2 Giorgos Tziafas 1 
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : We present a novel deductive parsing framework for categorial type logics, mo-deled as the composition of two components. The first is an attention-based neural supertagger, which assigns words dependency-decorated, contextually informed linear types. It requires no predefined type lexicon, instead utilizing the type syntax to construct types inductively, enabling the use of a richer and more precise typing environment. The type annotations produced are used by the second component, a computationally efficient hybrid system that emulates the inference process of the type logic, iteratively producing a bottom-up reconstruction of the input's derivation-proof and the associated program for compositional meaning assembly. Initial experiments yield promising results for each of the components.
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Submitted on : Friday, October 11, 2019 - 1:40:58 PM
Last modification on : Friday, August 5, 2022 - 3:03:22 PM


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  • HAL Id : lirmm-02313572, version 1



Konstantinos Kogkalidis, Michael Moortgat, Richard Moot, Giorgos Tziafas. Deductive Parsing with an Unbounded Type Lexicon. SEMSPACE, Aug 2019, Riga, Latvia. ⟨lirmm-02313572⟩



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