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• W2983004086 abstract "Contextualized word embeddings, i.e. vector representations for words in context, are naturally seen as an extension of previous noncontextual distributional semantic models. In this work, we focus on BERT, a deep neural network that produces contextualized embeddings and has set the state-of-the-art in several semantic tasks, and study the semantic coherence of its embedding space. While showing a tendency towards coherence, BERT does not fully live up to the natural expectations for a semantic vector space. In particular, we find that the position of the sentence in which a word occurs, while having no meaning correlates, leaves a noticeable trace on the word embeddings and disturbs similarity relationships." @default.
• W2983004086 created "2019-11-22" @default.
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• W2983004086 date "2020-01-01" @default.
• W2983004086 modified "2023-09-26" @default.
• W2983004086 title "What do you mean, BERT? Assessing BERT as a Distributional Semantics Model" @default.
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• W2983004086 doi "https://doi.org/10.7275/t778-ja71" @default.
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