The importance of modeling pragmatic syntactic bootstrapping
1Departments of Psychology and Linguistics, University of Southern California, USA.
Journal of Child Language
|September 22, 2023
Summary
Syntactic bootstrapping helps infer word meanings from sentence structure. This study explores its limits with abstract verbs and proposes pragmatic syntactic bootstrapping as a solution, suggesting computational modeling for further research.
Area of Science:
- Psycholinguistics
- Computational Linguistics
- Cognitive Science
Background:
- Syntactic bootstrapping links sentence structure to word meaning, aiding verb acquisition.
- Existing models face challenges with abstract verbs lacking observable referents, like attitude and modal verbs.
Purpose of the Study:
- To evaluate the limitations of syntactic bootstrapping for inferring meanings of abstract verbs.
- To explore the potential of pragmatic syntactic bootstrapping in addressing these limitations.
Main Methods:
- Analysis of Hacquard's (2022) keynote paper, which reviews behavioral experiments, corpus analyses, and computational models.
- Commentary suggesting computational modeling as a fruitful approach for evaluating and developing the proposed pragmatic syntactic bootstrapping account.
Main Results:
- Purely syntactic bootstrapping has limitations with abstract verbs (e.g., 'think', 'want', 'must').
- Augmenting syntactic information with pragmatic information may overcome these limitations.
Conclusions:
- Pragmatic syntactic bootstrapping offers a promising framework for understanding abstract verb acquisition.
- Further computational modeling is recommended to rigorously test and refine this account.
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