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.

本文提出了一种基于 Word2Vec 模型的新启发式方法来训练时间词嵌入，即使用不随时间变化的向量作为参考来简化训练过程以提高效率，并在现有数据集上进行的实验结果表明该方法比其他可比较方法表现更好且对语料库大小有更高的鲁棒性。