Scene Abstraction framework builds structured scene representations for lexical meaning via LLM prompting, with COCA-Scenes dataset and human experiments showing 82.4% identification accuracy and 86.4% preference over ATOMIC baselines.
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Graph-based neighborhood analysis of Persian poetry embeddings shows semantic change occurs through rewiring of local connections, with distinct patterns for time-sensitive, poet-sensitive, and stable words.
This survey paper identifies opportunities for LLMs in low-resource language humanities research along with challenges in data accessibility, model adaptability, and cultural sensitivity.
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Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaning
Scene Abstraction framework builds structured scene representations for lexical meaning via LLM prompting, with COCA-Scenes dataset and human experiments showing 82.4% identification accuracy and 86.4% preference over ATOMIC baselines.
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Between Century and Poet: Graph-Based Lexical Semantic Change in Persian Poetry
Graph-based neighborhood analysis of Persian poetry embeddings shows semantic change occurs through rewiring of local connections, with distinct patterns for time-sensitive, poet-sensitive, and stable words.
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Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
This survey paper identifies opportunities for LLMs in low-resource language humanities research along with challenges in data accessibility, model adaptability, and cultural sensitivity.
- Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection