One of the limits of word2vec is polysemy, which means that one word could have multiple meanings or senses. For example, the word “bank” could be a verb meaning “do financial work” or a noun meaning “financial institution”. And this problem is known in NLP by the name of “word-sense disambiguation”. In 2015, Andrew Trask proposed a model in his paper: Sense2Vec - A Fast And Accurate Method For Word Sense Disambiguation In Neural Word Embeddings. called “sense2vec”.

Sense2vec is a simple method to achieve word-sense disambiguation that leverages supervised labeling such as part-of-speech. The sense2vec model can learn different word senses of this word by combining a single-sense embedding model with POS labels as shown in the following figure:

Given a corpus, sense2vec will create a new corpus for each word for each sense by concatenating a word with its POS label. The new corpus is then trained using word2vec’s CBOW or skip-gram to create word embeddings that incorporate word sense (as it relates to their POS usage) as shown in the following figure: