Shohini Bhattasali


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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
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

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.

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Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension
Shohini Bhattasali | Philip Resnik
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension
Shohini Bhattasali | Jonathan Brennan | Wen-Ming Luh | Berta Franzluebbers | John Hale
Proceedings of the Twelfth Language Resources and Evaluation Conference

The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.

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Modeling conventionalization and predictability within MWEs at the brain level
Shohini Bhattasali | Murielle Fabre | Christophe Pallier | John Hale
Proceedings of the Society for Computation in Linguistics 2020


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Differentiating Phrase Structure Parsing and Memory Retrieval in the Brain
Shohini Bhattasali | John Hale | Christophe Pallier | Jonathan Brennan | Wen-Ming Luh | R. Nathan Spreng
Proceedings of the Society for Computation in Linguistics (SCiL) 2018

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Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs
Shohini Bhattasali | Murielle Fabre | John Hale
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

Multiword expressions have posed a challenge in the past for computational linguistics since they comprise a heterogeneous family of word clusters and are difficult to detect in natural language data. In this paper, we present a fMRI study based on language comprehension to provide neuroimaging evidence for processing MWEs. We investigate whether different MWEs have distinct neural bases, e.g. if verbal MWEs involve separate brain areas from non-verbal MWEs and if MWEs with varying levels of cohesiveness activate dissociable brain regions. Our study contributes neuroimaging evidence illustrating that different MWEs elicit spatially distinct patterns of activation. We also adapt an association measure, usually used to detect MWEs, as a cognitively plausible metric for language processing.


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Automatic Identification of Rhetorical Questions
Shohini Bhattasali | Jeremy Cytryn | Elana Feldman | Joonsuk Park
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)