The Processing Consequences of Compositionality

Baggio, Giosué; Lambalgen, Michiel van; Hagoort, Peter

doi:10.1093/oxfordhb/9780199541072.013.0032

Cited by 23 publications

(22 citation statements)

References 56 publications

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“…What the lambda term does here is merely lift the relevant expression from the object language to the metalanguage. A lexical meaning is just a placeholder for a typed entity [16,17], and meaning composition therefore reduces to logico-syntactic composition of placeholders for typed entities.…”

Section: Meaning Compositionmentioning

confidence: 99%

Modelling meaning composition from formalism to mechanism

Martin

Baggio

2019

Phil. Trans. R. Soc. B

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Human thought and language have extraordinary expressive power because meaningful parts can be assembled into more complex semantic structures. This partly underlies our ability to compose meanings into endlessly novel configurations, and sets us apart from other species and current computing devices. Crucially, human behaviour, including language use and linguistic data, indicates that composing parts into complex structures does not threaten the existence of constituent parts as independent units in the system: parts and wholes exist simultaneously yet independently from one another in the mind and brain. This independence is evident in human behaviour, but it seems at odds with what is known about the brain's exquisite sensitivity to statistical patterns: everyday language use is productive and expressive precisely because it can go beyond statistical regularities. Formal theories in philosophy and linguistics explain this fact by assuming that language and thought are compositional : systems of representations that separate a variable (or role ) from its values ( fillers ), such that the meaning of a complex expression is a function of the values assigned to the variables. The debate on whether and how compositional systems could be implemented in minds, brains and machines remains vigorous. However, it has not yet resulted in mechanistic models of semantic composition: how, then, are the constituents of thoughts and sentences put and held together? We review and discuss current efforts at understanding this problem, and we chart possible routes for future research. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

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Section: Meaning Compositionmentioning

confidence: 99%

Modelling meaning composition from formalism to mechanism

Martin

Baggio

2019

Phil. Trans. R. Soc. B

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“…Sentence comprehension therefore results from a "balance between storage and computation" (Baggio and Hagoort, 2011;Baggio et al, 2012) that simultaneously accounts for the unlimited possibility to understand new sentences, which are constructed by means of Unification, and for the processing advantage guaranteed by the retrieval from Memory of "ready-to-use" information about typical events and situations. Crucially, we argue that logical metonymy interpretation shares this same mechanism of on-line sentence processing and that the covert event is i.)…”

Section: A Distributional Model Of Sentence Comprehensionmentioning

confidence: 99%

Logical Metonymy in a Distributional Model of Sentence Comprehension

Chersoni¹,

Lenci²,

Blache³

2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

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In theoretical linguistics, logical metonymy is defined as the combination of an event-subcategorizing verb with an entity-denoting direct object (e.g., The author began the book), so that the interpretation of the VP requires the retrieval of a covert event (e.g., writing). Psycholinguistic studies have revealed extra processing costs for logical metonymy, a phenomenon generally explained with the introduction of new semantic structure. In this paper, we present a general distributional model for sentence comprehension inspired by the Memory, Unification and Control model by Hagoort (2013Hagoort ( , 2016. We show that our distributional framework can account for the extra processing costs of logical metonymy and can identify the covert event in a classification task.

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Section: A Distributional Model Of Sentence Comprehensionmentioning

confidence: 99%