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• W3113668904 abstract "We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling. Our system focuses on Vector Initialization (VI) alignment, compares VI to the currently top-ranking models for Subtask 2 and demonstrates that these can be outperformed if we optimize VI dimensionality. We demonstrate that differences in performance can largely be attributed to model-specific sources of noise, and we reveal a strong relationship between dimensionality and frequency-induced noise in VI alignment. Our results suggest that lexical semantic change models integrating vector space alignment should pay more attention to the role of the dimensionality parameter." @default.
• W3113668904 created "2021-01-05" @default.
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• W3113668904 date "2020-01-01" @default.
• W3113668904 modified "2023-10-16" @default.
• W3113668904 title "IMS at SemEval-2020 Task 1: How Low Can You Go? Dimensionality in Lexical Semantic Change Detection" @default.
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• W3113668904 doi "https://doi.org/10.18653/v1/2020.semeval-1.8" @default.
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