arXiv:2605.23710v1 Announce Type: new 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
The continuous evolution of large language models and their contextual understanding necessitates deeper analysis of how they process semantic nuances, especially in areas like coercion.
Improved understanding of contextualized word embeddings can lead to more robust and accurate AI applications, reducing errors in natural language processing and generation.
This research provides a new methodology for analyzing semantic types and coercion, potentially improving the interpretability and reliability of AI models' language comprehension.
- · AI researchers
- · NLP developers
- · AI ethics and safety organizations
- · Developers relying solely on superficial word embedding analysis
Refined understanding of how language models process complex semantic relationships.
Development of more sophisticated AI models capable of handling nuanced linguistic phenomena with greater accuracy.
Enhanced trust in AI systems performing sensitive language-based tasks due to improved semantic reasoning.
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