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 …

Recent years have witnessed a surge of publications aimed at tracing temporal changes in
lexical semantics using distributional methods, particularly prediction-based word …

We propose a new computational approach for tracking and detecting statistically significant
linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially …

Word evolution refers to the changing meanings and associations of words throughout time,
as a byproduct of human language evolution. By studying word evolution, we can infer …

Word embeddings are a powerful approach for unsupervised analysis of language.
Recently, Rudolph et al. developed exponential family embeddings, which cast word …

This article evaluates three proposed laws of semantic change. Our claim is that in order to
validate a putative law of semantic change, the effect should be observed in the genuine …

State-of-the-art models of lexical semantic change detection suffer from noise stemming from
vector space alignment. We have empirically tested the Temporal Referencing method for …

Diachronic word embeddings have been widely used in detecting temporal changes.
However, existing methods face the meaning conflation deficiency by representing a word …

The problem of comparing two bodies of text and searching for words that differ in their
usage between them arises often in digital humanities and computational social science …

Word meanings change over time and an automated procedure for extracting this
information from text would be useful for historical exploratory studies, information retrieval …