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  3. NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fin
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NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

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Freshness: 2026-04-02T02:30:40.136932+00:00

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References: 0

Proof: pending

Distribution: unknown

Source paper: NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

PDF: https://arxiv.org/pdf/2603.08256v1

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Prior Work
QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysis
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Higher Viability
An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs
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Competing Approach
COGNAC at SemEval-2026 Task 5: LLM Ensembles for Human-Level Word Sense Plausibility Rating in Challenging Narratives
Score 3.0down
Competing Approach
To Words and Beyond: Probing Large Language Models for Sentence-Level Psycholinguistic Norms of Memorability and Reading Times
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Competing Approach
In the LLM era, Word Sense Induction remains unsolved
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