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New graph method analyzes semantic types in word embeddings

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel graph-based approach to analyze how semantic type information is represented within contextualized word embeddings. This method uses metrics like Neighbor Type Probability (NTP) and Neighbor Type Entropy (NTE) to examine the distribution of semantic types in the embeddings' neighborhoods. The study found that sense-enhanced embeddings better capture lexical and contextual type information, enabling the distinction between sentences with matching and mismatching semantic types. AI

IMPACT Introduces a new analytical framework for understanding the nuances of word embeddings, potentially improving downstream NLP tasks.

RANK_REASON The cluster contains an academic paper detailing a new research methodology.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Long Chen, Deniz Ekin Yavas ·

    A graph-based analysis of semantic types and coercion in contextualized word embeddings

    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 nou…

  2. arXiv cs.CL TIER_1 · Deniz Ekin Yavas ·

    A graph-based analysis of semantic types and coercion in contextualized word embeddings

    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 inst…

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