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.

该研究通过发展一种动态统计模型学习时态感知的词向量表示，并实现了 “对齐问题” 的解决，从而可以可靠地捕捉时间上的语言演变，并在语义准确性和对齐质量方面持续优于现有的时间嵌入方法。