From cart to truck: meaning shift through words in English in the last two centuries
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Abstract (arXiv)
This onomasiological study uses diachronic word embeddings to explore how different words represented the same concepts over time, using historical word data from 1800 to 2000. We identify shifts in energy, transport, entertainment, and computing domains, revealing connections between language and societal changes. Our approach consisted in using diachronic word embeddings trained using word2vec with skipgram and aligning them using orthogonal Procrustes. We discuss possible difficulties linked to the relationships the method identifies. Moreover, we look at the ethical aspects of interpreting results, highlighting the need for expert insights to understand the method's significance.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 29 Aug 2024.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
- 2014Distributed Representations of Sentences and Documentscited by 677
- 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
- 2018Diachronic word embeddings and semantic shifts: a surveycited by 90
- 2021Challenges for Computational Lexical Semantic Changecited by 15
- 2019One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news textscited by 2
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