Word2vec is one of the most used algorithms to generate word embeddings
because of a good mix of efficiency, quality of the generated representations
and cognitive grounding. However, word meaning is not static and depends on the
context in which words are used. Differences in word meaning that depends on
time, location, topic, and other factors, can be studied by analyzing
embeddings generated from different corpora in collections that are
representative of these factors. For example, language evolution can be studied
using a collection of news articles published in different time periods. In
this paper, we present a general framework to support cross-corpora language
studies with word embeddings, where embeddings generated from different corpora
can be compared to find correspondences and differences in meaning across the
corpora. CADE is the core component of our framework and solves the key problem
of aligning the embeddings generated from different corpora. In particular, we
focus on providing solid evidence about the effectiveness, generality, and
robustness of CADE. To this end, we conduct quantitative and qualitative
experiments in different domains, from temporal word embeddings to language
localization and topical analysis. The results of our experiments suggest that
CADE achieves state-of-the-art or superior performance on tasks where several
competing approaches are available, yet providing a general method that can be
used in a variety of domains. Finally, our experiments shed light on the
conditions under which the alignment is reliable, which substantially depends
on the degree of cross-corpora vocabulary overlap.

本文提出了一个通用框架，用于支持使用单词嵌入进行跨语料库的语言研究，其中可以比较从不同语料库生成的嵌入项，以找出语料库之间的含义对应和差异。该框架的核心组件是 CADE，重点解决了从不同语料库生成的嵌入项的对齐问题。经过实验，结果表明 CADE 在时间词嵌入，语言本地化和主题分析等各个领域都达到了最先进或更优异的性能。