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        <title>Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser</title>
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        <namePart type="given">Aniello</namePart>
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            <namePart type="given">Tatsuki</namePart>
            <namePart type="family">Kuribayashi</namePart>
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        <name type="personal">
            <namePart type="given">Giulia</namePart>
            <namePart type="family">Rambelli</namePart>
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        <name type="personal">
            <namePart type="given">Ece</namePart>
            <namePart type="family">Takmaz</namePart>
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        <name type="personal">
            <namePart type="given">Philipp</namePart>
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            <namePart type="given">Jixing</namePart>
            <namePart type="family">Li</namePart>
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            <namePart type="given">Byung-Doh</namePart>
            <namePart type="family">Oh</namePart>
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    <abstract>A parser for Minimalist grammars (Stabler, 2013) has been shown to successfully model sentence processing preferences across an array of languages and phenomena when combined with complexity metrics that relate parsing behavior to memory usage (Gerth, 2015; Graf et al., 2017; De Santo, 2020, a.o.). This model provides a quantifiable theory of the effects of fine-grained grammatical structure on cognitive cost, and can help strengthen the link between generative syntactic theory and sentence processing.However, work on it has focused on offline asymmetries.Here, we extend this approach by showing how memory-based measures of effort that explicitly consider minimalist-like structure-building operations improve our ability to account for word-by-word (online) behavioral data.</abstract>
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%0 Conference Proceedings
%T Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser
%A De Santo, Aniello
%Y Kuribayashi, Tatsuki
%Y Rambelli, Giulia
%Y Takmaz, Ece
%Y Wicke, Philipp
%Y Li, Jixing
%Y Oh, Byung-Doh
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-227-5
%F de-santo-2025-capturing
%X A parser for Minimalist grammars (Stabler, 2013) has been shown to successfully model sentence processing preferences across an array of languages and phenomena when combined with complexity metrics that relate parsing behavior to memory usage (Gerth, 2015; Graf et al., 2017; De Santo, 2020, a.o.). This model provides a quantifiable theory of the effects of fine-grained grammatical structure on cognitive cost, and can help strengthen the link between generative syntactic theory and sentence processing.However, work on it has focused on offline asymmetries.Here, we extend this approach by showing how memory-based measures of effort that explicitly consider minimalist-like structure-building operations improve our ability to account for word-by-word (online) behavioral data.
%R 10.18653/v1/2025.cmcl-1.5
%U https://aclanthology.org/2025.cmcl-1.5/
%U https://doi.org/10.18653/v1/2025.cmcl-1.5
%P 24-35

Markdown (Informal)

[Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser](https://aclanthology.org/2025.cmcl-1.5/) (De Santo, CMCL 2025)

• Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser (De Santo, CMCL 2025)

ACL

• Aniello De Santo. 2025. Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 24–35, Albuquerque, New Mexico, USA. Association for Computational Linguistics.