  Temporal word embeddings have been proposed to support the analysis of word
meaning shifts during time and to study the evolution of languages. Different
approaches have been proposed to generate vector representations of words that
embed their meaning during a specific time interval. However, the training
process used in these approaches is complex, may be inefficient or it may
require large text corpora. As a consequence, these approaches may be difficult
to apply in resource-scarce domains or by scientists with limited in-depth
knowledge of embedding models. In this paper, we propose a new heuristic to
train temporal word embeddings based on the Word2vec model. The heuristic
consists in using atemporal vectors as a reference, i.e., as a compass, when
training the representations specific to a given time interval. The use of the
compass simplifies the training process and makes it more efficient.
Experiments conducted using state-of-the-art datasets and methodologies suggest
that our approach outperforms or equals comparable approaches while being more
robust in terms of the required corpus size.
