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ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch

Konstantinos Kogkalidis 1 Michael Moortgat 1 Richard Moot 2
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : We present {\AE}THEL, a semantic compositionality dataset for written Dutch. {\AE}THEL consists of two parts. First, it contains a lexicon of supertags for about 900 000 words in context. The supertags correspond to types of the simply typed linear lambda-calculus, enhanced with dependency decorations that capture grammatical roles supplementary to function-argument structures. On the basis of these types, {\AE}THEL further provides 72 192 validated derivations, presented in four formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. {\AE}THEL's types and derivations are obtained by means of an extraction algorithm applied to the syntactic analyses of LASSY Small, the gold standard corpus of written Dutch. We discuss the extraction algorithm and show how `virtual elements' in the original LASSY annotation of unbounded dependencies and coordination phenomena give rise to higher-order types. We suggest some example usecases highlighting the benefits of a type-driven approach at the syntax semantics interface. The following resources are open-sourced with {\AE}THEL: the lexical mappings between words and types, a subset of the dataset consisting of 7 924 semantic parses, and the Python code that implements the extraction algorithm.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-02916423
Contributor : Richard Moot <>
Submitted on : Monday, August 17, 2020 - 6:40:26 PM
Last modification on : Thursday, August 20, 2020 - 3:27:43 AM

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  • HAL Id : lirmm-02916423, version 1
  • ARXIV : 1912.12635

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Konstantinos Kogkalidis, Michael Moortgat, Richard Moot. ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch. 25th International Workshop on Language Resources and Evaluation Conference (LREC), May 2020, Marseille, France. ⟨lirmm-02916423⟩

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