  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 social trends and language constructs over different
periods of human history. However, traditional techniques such as word
representation learning do not adequately capture the evolving language
structure and vocabulary. In this paper, we develop a dynamic statistical model
to learn time-aware word vector representation. We propose a model that
simultaneously learns time-aware embeddings and solves the resulting "alignment
problem". This model is trained on a crawled NYTimes dataset. Additionally, we
develop multiple intuitive evaluation strategies of temporal word embeddings.
Our qualitative and quantitative tests indicate that our method not only
reliably captures this evolution over time, but also consistently outperforms
state-of-the-art temporal embedding approaches on both semantic accuracy and
alignment quality.
