  Word embeddings are powerful dictionaries, which may easily capture language
variations. However, these dictionaries fail to give sense to rare words, which
are surprisingly often covered by traditional dictionaries. In this paper, we
propose to use definitions retrieved in traditional dictionaries to produce
word embeddings for rare words. For this purpose, we introduce two methods:
Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our
experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as
well as baseline methods devised for producing embeddings of unknown words. In
fact, DefiNNet significantly outperforms FastText, which implements a method
for the same task-based on n-grams, and DefBERT significantly outperforms the
BERT method for OOV words. Then, definitions in traditional dictionaries are
useful to build word embeddings for rare words.
