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    <abstract>This paper introduces a usage-based framework that models argument structure annotation as nearest-neighbor classification over verb–argument structure (VAS) embeddings. Instead of parsing sentences separately, the model aligns new tokens with previously observed constructions in an embedding space derived from semi-automatic corpus annotations. Pilot studies show that cosine similarity captures both form and meaning, that nearest-neighbor classification generalizes to dative alternation verbs, and that accuracy in locative alternation depends on the corpus source of exemplars. These results suggest that analogical classification is shaped by both structural similarity and corpus alignment, highlighting key considerations for scalable, construction-based annotation of new sentence inputs.</abstract>
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%0 Conference Proceedings
%T Annotating English Verb-Argument Structure via Usage-Based Analogy
%A Hsiao, Allen Minchun
%A Michaelis, Laura A.
%Y Bonial, Claire
%Y Torgbi, Melissa
%Y Weissweiler, Leonie
%Y Blodgett, Austin
%Y Beuls, Katrien
%Y Van Eecke, Paul
%Y Tayyar Madabushi, Harish
%S Proceedings of the Second International Workshop on Construction Grammars and NLP
%D 2025
%8 September
%I Association for Computational Linguistics
%C Düsseldorf, Germany
%@ 979-8-89176-318-0
%F hsiao-michaelis-2025-annotating
%X This paper introduces a usage-based framework that models argument structure annotation as nearest-neighbor classification over verb–argument structure (VAS) embeddings. Instead of parsing sentences separately, the model aligns new tokens with previously observed constructions in an embedding space derived from semi-automatic corpus annotations. Pilot studies show that cosine similarity captures both form and meaning, that nearest-neighbor classification generalizes to dative alternation verbs, and that accuracy in locative alternation depends on the corpus source of exemplars. These results suggest that analogical classification is shaped by both structural similarity and corpus alignment, highlighting key considerations for scalable, construction-based annotation of new sentence inputs.
%U https://aclanthology.org/2025.cxgsnlp-1.16/
%P 158-164

Markdown (Informal)

[Annotating English Verb-Argument Structure via Usage-Based Analogy](https://aclanthology.org/2025.cxgsnlp-1.16/) (Hsiao & Michaelis, CxGsNLP 2025)

• Annotating English Verb-Argument Structure via Usage-Based Analogy (Hsiao & Michaelis, CxGsNLP 2025)

ACL

• Allen Minchun Hsiao and Laura A. Michaelis. 2025. Annotating English Verb-Argument Structure via Usage-Based Analogy. In Proceedings of the Second International Workshop on Construction Grammars and NLP, pages 158–164, Düsseldorf, Germany. Association for Computational Linguistics.