  Word embedding is a fundamental natural language processing task which can
learn feature of words. However, most word embedding methods assign only one
vector to a word, even if polysemous words have multi-senses. To address this
limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different
vector to every sense of polysemous words with the help of word sense
disambiguation (WSD) and synonym set in OpenHowNet. We use the SememeWSD model,
an unsupervised word sense disambiguation model based on OpenHowNet, to do word
sense disambiguation and annotate the polysemous word with sense id. Then, we
obtain top 10 synonyms of the word sense from OpenHowNet and calculate the
average vector of synonyms as the vector of the word sense. In experiments, We
evaluate the SWSDS model on semantic similarity calculation with Gensim's
wmdistance method. It achieves improvement of accuracy. We also examine the
SememeWSD model on different BERT models to find the more effective model.
