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Hauptverfasser: Kaipeng, Liu, Ling, Wu
Format: Preprint
Veröffentlicht: 2026
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Online-Zugang:https://arxiv.org/abs/2602.00554
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author Kaipeng, Liu
Ling, Wu
author_facet Kaipeng, Liu
Ling, Wu
contents This study investigates how the Bidirectional Encoder Representations from Transformers model processes four fundamental Argument Structure Constructions. We employ a multi-dimensional analytical framework, which integrates MDS, t-SNE as dimensionality reduction, Generalized Discrimination Value (GDV) as cluster separation metrics, Fisher Discriminant Ratio (FDR) as linear diagnostic probing, and attention mechanism analysis. Our results reveal a hierarchical representational structure. Construction-specific information emerges in early layers, forms maximally separable clusters in middle layers, and is maintained through later processing stages.
format Preprint
id arxiv_https___arxiv_org_abs_2602_00554
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Hierarchical and Attentional Analysis of Argument Structure Constructions in BERT Using Naturalistic Corpora
Kaipeng, Liu
Ling, Wu
Computation and Language
J.5.5
This study investigates how the Bidirectional Encoder Representations from Transformers model processes four fundamental Argument Structure Constructions. We employ a multi-dimensional analytical framework, which integrates MDS, t-SNE as dimensionality reduction, Generalized Discrimination Value (GDV) as cluster separation metrics, Fisher Discriminant Ratio (FDR) as linear diagnostic probing, and attention mechanism analysis. Our results reveal a hierarchical representational structure. Construction-specific information emerges in early layers, forms maximally separable clusters in middle layers, and is maintained through later processing stages.
title A Hierarchical and Attentional Analysis of Argument Structure Constructions in BERT Using Naturalistic Corpora
topic Computation and Language
J.5.5
url https://arxiv.org/abs/2602.00554