Essentially they are useful for comparing the semantic similarity of pieces of text. The text could be a word, phrase, sentence, paragraph, or document. One practical use case is semantic keyword search where the vectors can be used to automatically find a keyword's synonyms. Another is recommendation engines that recommend other documents based on semantic similarity.
are you sure it allows to guess synonyms? I was under the impression that word2vec only allowed to know how similar are words, which different from synonyms. E.g. red is like blue in word2vec sens, but not a synonym.
Technically yes. It will find words which are used in similar contexts such as synonyms, antonyms, etc. However in practice, word2vec and clustering does a good job of finding synonyms [1].
Was very pleased to find this out when I first started studying word embeddings (the abstract principles of word2vec). Essentially it comes down to words having similar verbs and objects that come up most frequently together, so they end up being semantically close.
