· 2016
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
- 869citations
- 2016year
Short summary
Researchers developed a robust method to quantify semantic change in words over time using word embeddings and historical corpora, revealing two statistical laws governing how word meanings evolve.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed a robust methodology to quantify semantic change using word embeddings and historical data.
- Evaluated existing embedding methods (PPMI, SVD, word2vec) against known historical meaning shifts.
- Proposed the 'law of conformity': semantic change rate scales with an inverse power-law of word frequency.
- Proposed the 'law of innovation': polysemous words have higher rates of semantic change, independent of frequency.
- Analyzed six historical corpora across four languages and two centuries.
AI-generated from the title and abstract; the full text is not read.
Abstract
Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test. Word embeddings show promise as a diachronic tool, but have not been carefully evaluated. We develop a robust methodology for quantifying semantic change by evaluating word embeddings (PPMI, SVD, word2vec) against known historical changes. We then use this methodology to reveal statistical laws of semantic evolution. Using six historical corpora spanning four languages and two centuries, we propose two quantitative laws of semantic change: (i) the law of conformity-the rate of semantic change scales with an inverse power-law of word frequency; (ii) the law of innovation-independent of frequency, words that are more polysemous have higher rates of semantic change.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Cultural Studies
Cultural StudiesSocial Sciences