Modeling incremental language comprehension in the brain with Combinatory Categorial Grammar

Miloš Stanojević, Shohini Bhattasali, Donald Dunagan, Luca Campanelli, Mark Steedman, Jonathan Brennan, John Hale

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model. With a view towards sharpening this picture, we model the time course of hemodynamic activity within the brain during an extended episode of naturalistic language comprehension using Combinatory Categorial Grammar (CCG). CCG has well-defined incremental parsing algorithms, surface compositional semantics, and can explain long-range dependencies as well as complicated cases of coordination. We find that CCG-derived predictors improve a regression model of fMRI time course in six language-relevant brain regions, over and above predictors derived from context-free phrase structure. Adding a special Revealing operator to CCG parsing, one designed to handle right-adjunction, improves the fit in three of these regions. This evidence for CCG from neuroimaging bolsters the more general case for mildly context-sensitive grammars in the cognitive science of language.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Cognitive Modeling and Computational Linguistics
PublisherAssociation for Computational Linguistics
Pages23–38
Number of pages16
ISBN (Print)978-1-954085-35-0
DOIs
Publication statusPublished - 10 Jun 2021
EventThe 2021 Cognitive Modeling and Computational Linguistics workshop - Mexico City, Mexico
Duration: 10 Jun 202110 Jun 2021
https://cmclorg.github.io/

Workshop

WorkshopThe 2021 Cognitive Modeling and Computational Linguistics workshop
Abbreviated titleCMCL 2021
Country/TerritoryMexico
CityMexico City
Period10/06/2110/06/21
Internet address

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