about gensim word2vec most_similar() function

[sam]

I'm using the most_similar() method as below to get all the words similar to a given word:

word,score= model.most_similar('apple',topn=sizeofdict)

AFAIK, what this does is, calculate the cosine similarity between the given word and all the other words in the dictionary. When i'm inspecting the words and scores, I can see there are words with negative score down the list. What does this mean? are them the words that has opposite meaning to the given word?

Also if it's using cosine similarity, how does it get a negative value? cosine similarity varies between 0-1 for two documents.

[Radim Řehůřek]

Hello sam,

yes, you could interpret negative scores as "opposite meaning". Sometimes, for specific applications of similarity, people take absolute value of the cosine score as well. This can be interpreted as "I want to treat opposites as similar".

Cosine similarity has a range of <-1, 1> (not <0, 1>).

HTH,

Radim