Understanding cosine distance with word vectors
Word2Vec algorithm does not go inside words. Word “king” is never used as a gerund, so there is no reason why it should be similar to gerunds.

My guesses are:


Your corpus might by wrongly tokenized. Maybe there are some OCR-related errors with word splitting something like “li-↲ king”.
You might be using a different algorithm for getting the embeddings (e.g., FastText) that goes inside the words and infers the word embedding as some embeddings of character n-grams the word consists of.


On the other hand, words similar to man look fine. If you think about how Word2Vec (and also FastText) is trained, you should not ask a question: ”Do the words have as similar meaning as possible?” but rather “Does the word appear similarly frequently in a similar context Shakespear's works?”

(Of course, when the embeddings are trained on data which is large enough, there is almost no difference between these two questions.)