  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.
