Semantic Change and Semantic Stability: Variation is Key
I survey some recent approaches to studying change in the lexicon, particularly change in meaning across phylogenies. I briefly sketch an evolutionary approach to language change and point out some issues in recent approaches to studying semantic change that rely on temporally stratified word embeddings. I draw illustrations from lexical cognate models in Pama-Nyungan to identify meaning classes most appropriate for lexical phylogenetic inference, particularly highlighting the importance of variation in studying change over time.
Code (0)
등록된 구현이 없습니다.
Tasks
SurveyWord EmbeddingsSimilar Papers 제목 키워드 기반
Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis
Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effective…
ArticlesDistributional Semantics, Holism, and the Instability of Meaning
Current language models are built on the so-called distributional semantic approach to linguistic meaning that has the distributional hypothesis at its core. The distributional hypothesis involves a holistic conception o…
ArticlesFoundation Model-Driven Semantic Change Detection in Remote Sensing Imagery
Remote sensing (RS) change detection is essential for interpreting surface dynamics. Semantic change detection (SCD) further enables pixel-level understanding of multi-class transitions, yet remains sensitive to pseudo-c…
Change DetectionCalibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations
Confidence estimation (CE) indicates how reliable the answers of large language models are and impacts user trust and decision-making. Existing evaluations mainly concern the alignment between confidence and correctness,…
Evaluating Semantic Fragility in Text-to-Audio Generation Systems Under Controlled Prompt Perturbations
Recent advances in text-to-audio generation enable models to translate natural-language descriptions into diverse musical output. However, the robustness of these systems under semantically equivalent prompt variations r…
Semantic SimilarityAudio Generation