A graph-based analysis of semantic types and coercion in contextualized word embeddings
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Computer Science > Computation and Language
Title:A graph-based analysis of semantic types and coercion in contextualized word embeddings
Abstract:Semantic type mismatch between a noun and its context is central to coercion phenomena. This paper introduces a graph-based method to examine how lexical and contextual type information is reflected in word embeddings. We select nouns from ten semantic types, annotate corpus instances for type matching (matching vs. coercion vs. other mismatch vs. unrestricted), and construct graphs using BERT and sense-enhanced embeddings. Two metrics -- Neighbor Type Probability (NTP) and Neighbor Type Entropy (NTE) -- are proposed to analyze neighborhood type distributions. Results show that graphs constructed with sense-enhanced embeddings reflect semantic type information better, and matching and mismatch sentences can be distinguished through the proposed metrics.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.23710 [cs.CL] |
| (or arXiv:2605.23710v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23710
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Deniz Ekin Yavas [view email][v1] Fri, 22 May 2026 14:55:54 UTC (2,431 KB)
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