Gradient HPSG

Prevailing grammatical frameworks treat grammaticality as a binary concept, despite strong experimental evidence suggesting it is better understood as a gradient notion. This highlights a serious disconnect between linguistic theory and empirical data. While a few truly gradient frameworks have been proposed to bridge the gap, none have been developed within a constraint-based formalism – an approach particularly well-suited for modeling gradient grammaticality. This work formally introduces a gradient version of HPSG and subsequently employs it to analyze acceptability judgment data on unlike coordination phenomena in Turkish, which display distinctly gradient patterns.

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Publication Details

Journal
Proceedings of the International Conference on Head-Driven Phrase Structure Grammar
Published
2024-12-11
DOI
https://doi.org/10.21248/hpsg.2024.11
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Gradient HPSG

Berke Şenşekerci
Proceedings of the International Conference on Head-Driven Phrase Structure Grammar
Radiomics and Machine Learning in Medical Imaging
article

Gradient HPSG

Berke Şenşekerci
article en

Abstract

Prevailing grammatical frameworks treat grammaticality as a binary concept, despite strong experimental evidence suggesting it is better understood as a gradient notion. This highlights a serious disconnect between linguistic theory and empirical data. While a few truly gradient frameworks have been proposed to bridge the gap, none have been developed within a constraint-based formalism – an approach particularly well-suited for modeling gradient grammaticality. This work formally introduces a gradient version of HPSG and subsequently employs it to analyze acceptability judgment data on unlike coordination phenomena in Turkish, which display distinctly gradient patterns.

Proceedings of the International Conference on Head-Driven Phrase Structure Grammar
University of Warsaw (PL)
Openalex Percentile: Top 41%
Radiomics and Machine Learning in Medical Imaging
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