gensim.models.Word2Vec.most_similar_cosmul¶

Word2Vec.most_similar_cosmul(positive=[], negative=[], topn=10)[source]¶

Find the top-N most similar words, using the multiplicative combination objective proposed by Omer Levy and Yoav Goldberg in [R10]. Positive words still contribute positively towards the similarity, negative words negatively, but with less susceptibility to one large distance dominating the calculation.

In the common analogy-solving case, of two positive and one negative examples, this method is equivalent to the “3CosMul” objective (equation (4)) of Levy and Goldberg.

Additional positive or negative examples contribute to the numerator or denominator, respectively – a potentially sensible but untested extension of the method. (With a single positive example, rankings will be the same as in the default most_similar.)

Example:

>>> trained_model.most_similar_cosmul(positive=['baghdad', 'england'], negative=['london'])
[(u'iraq', 0.8488819003105164), ...]

[R10]

Omer Levy and Yoav Goldberg. Linguistic Regularities in Sparse and Explicit Word Representations, 2014.