Finally, Press and Wolf (2017) introduced a model, based on word2vec, where the embeddings are extracted from the output topmost weight matrix, instead of the input one, showing that those representations are also valid word embeddings.
to the above approaches, each of which aims to learn representations of lexical items, sense embeddings represent individual word senses as separate vectors.