Doc2Vec.most_similar(positive=[], negative=[], topn=10, restrict_vocab=None, indexer=None)

Find the top-N most similar words. Positive words contribute positively towards the similarity, negative words negatively.

This method computes cosine similarity between a simple mean of the projection weight vectors of the given words and the vectors for each word in the model. The method corresponds to the word-analogy and distance scripts in the original word2vec implementation.

If topn is False, most_similar returns the vector of similarity scores.

restrict_vocab is an optional integer which limits the range of vectors which are searched for most-similar values. For example, restrict_vocab=10000 would only check the first 10000 word vectors in the vocabulary order. (This may be meaningful if you’ve sorted the vocabulary by descending frequency.)


>>> trained_model.most_similar(positive=['woman', 'king'], negative=['man'])
[('queen', 0.50882536), ...]