Modeling Conventionalization and Predictability within MWEs at the Brain Level
- Shohini Bhattasali (University of Maryland, College Park)
- Murielle Popa-Fabre (INRIA - University of Paris)
- Christophe Pallier (INSERM-CEA)
- John Hale (University of Georgia)
Abstract
While expressions have traditionally been binarized as compositional and noncompositional in linguistic theory, Multiword Expressions (MWEs) demonstrate finer-grained distinctions. Using Association Measures like Pointwise Mutual Information and Dice\'s Coefficient, MWEs can be characterized as having different degrees of conventionalization and predictability. Our goal is to investigate how these gradiences could reflect cognitive processes. In this study, fMRI recordings of naturalistic narrative comprehension is used to probe to what extent these computational measures and the cognitive processes they could operationalize are observable during on-line sentence processing. Our results show that Dice\'s Coefficent, representing lexical predictability, is a better predictor of neural activation for processing MWEs. Overall our experimental approach demonstrates how we can test the cognitive plausibility of computational metrics by comparing it against neuroimaging data.
Keywords: MWES, predictability, conventionalization, fMRI, pmi, dice, compositionality
How to Cite:
Bhattasali, S., Popa-Fabre, M., Pallier, C. & Hale, J., (2020) “Modeling Conventionalization and Predictability within MWEs at the Brain Level”, Society for Computation in Linguistics 3(1), 110-119. doi: https://doi.org/10.7275/metk-r062
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