Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

Understand

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.