A type-logical treebank for French

Moot, Richard

doi:10.15398/jlm.v3i1.92

Cited by 16 publications

(14 citation statements)

References 18 publications

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“…They compute the possible meanings of a sentence viewed as logical formulae. In particular, the Grail platform, a wide-scale categorial parser which maps French sentences to logical formulas [31,30,32], will be presented. We then use Grail and dialogical logic to solve some examples of textual entailment from the FraCaS database [11].…”

Section: Inferentialism and Textual Entailmentmentioning

confidence: 99%

Dialogical Argumentation and Textual Entailment

Catta

Moot

Retoré

2021

Studies in Computational Intelligence

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In this chapter, we introduce a new dialogical system for first order classical logic which is close to natural language argumentation, and we prove its completeness with respect to usual classical validity. We combine our dialogical system with the Grail syntactic and semantic parser developed by the second author in order to address automated textual entailment, that is, we use it for deciding whether or not a sentence is a consequence of a short text. This work -which connects natural language semantics and argumentation with dialogical logic -can be viewed as a step towards an inferentialist view of natural language semantics.

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Section: Inferentialism and Textual Entailmentmentioning

confidence: 99%

Dialogical Argumentation and Textual Entailment

Catta

Moot

Retoré

2021

Studies in Computational Intelligence

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“…However, when we fix a set of possible formulas and have enough examples of sentences with the correct formula assignment, we can define a probability model [ 300 ] over words, together with a limited amount of context (typically the two preceding and succeeding words). This general approach is called supertagging (Bangalore and Joshi 2011) and it has been applied successfully to many formalisms including type-logical grammars (Moot 2010(Moot , 2014b, presents supertaggers for multimodal type-logical grammars for Dutch and for French).…”

Section: A Probabilistic Lexiconmentioning

confidence: 99%

Combining logical and distributional methods in type-logical grammars

Moot

2019

JLM

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We propose a low-level way of combining distributional and logical ideas into a single formal system. This will be an instantiation of a more general system, adding weights to proof rules. These weights will not measure some sort of "confidence the proof is valid", but rather act as a way to prefer some proofs over others, where preference can mean "easier to process (for humans)" or "more coherent (combining words that make sense together)". The resulting system of weighted theorem proving can be implemented either as a best-first proof search strategy or as a polynomial-time approximation of proof search for NP-complete parsing problems. 1 introduction Type-logical grammars (and formal semantics in general) are agnostic about the meaning of atomic terms, such as those corresponding to nouns and verbs (though not about the meaning corresponding to words with logical content such as "not", "and", "all", "which"). Another way to see this is that in standard formal semantics, entailment only holds under strict identity of predicates. As a consequence, practical use of the output of a system computing such formal semantics depends to a large extent on the available world knowledge (Bos and Markert 2005), possibly stated in the form of additional axioms or meaning postulates, stating that "pub'' and "bar'' (in one meaning of the word) are synonyms, and that "good" and "bad" are antonyms, i.e. "bad" entails "not good" and inversely.

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“…The formulation of the extraction process takes its inspiration from (Moortgat and Moot, 2002;Moot, 2015), modulo some adaptations to account for the discrepancies in the type-logic and the source corpus. Algorithmically, it may be perceived as a pipeline of three distinct components.…”

Section: Extractionmentioning

confidence: 99%

ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch

Kogkalidis¹,

Moortgat²,

Moot³

2019

Preprint

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We present AETHEL, a semantic compositionality dataset for written Dutch. AETHEL 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, AETHEL further provides 72 623 validated derivations, presented in four equivalent formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. AETHEL'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 AETHEL: the lexical mappings between words and types, a subset of the dataset consisting of 8 569 semantic parses, and the Python code that implements the extraction algorithm.

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A type-logical treebank for French

Cited by 16 publications

References 18 publications

Dialogical Argumentation and Textual Entailment

Dialogical Argumentation and Textual Entailment

Combining logical and distributional methods in type-logical grammars

ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch

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