  Language is dynamic, constantly evolving and adapting with respect to time,
domain or topic. The adaptability of language is an active research area, where
researchers discover social, cultural and domain-specific changes in language
using distributional tools such as word embeddings. In this paper, we introduce
the global anchor method for detecting corpus-level language shifts. We show
both theoretically and empirically that the global anchor method is equivalent
to the alignment method, a widely-used method for comparing word embeddings, in
terms of detecting corpus-level language shifts. Despite their equivalence in
terms of detection abilities, we demonstrate that the global anchor method is
superior in terms of applicability as it can compare embeddings of different
dimensionalities. Furthermore, the global anchor method has implementation and
parallelization advantages. We show that the global anchor method reveals fine
structures in the evolution of language and domain adaptation. When combined
with the graph Laplacian technique, the global anchor method recovers the
evolution trajectory and domain clustering of disparate text corpora.
