How to calculate the sentence similarity using word2vec model of gensim with python

Question

According to the Gensim Word2Vec, I can use the word2vec model in gensim package to calculate the similarity between 2 words.

e.g.

trained_model.simi         


        

Answer 1

I would like to update the existing solution to help the people who are going to calculate the semantic similarity of sentences.

Step 1:

Load the suitable model using gensim and calculate the word vectors for words in the sentence and store them as a word list

Step 2 : Computing the sentence vector

The calculation of semantic similarity between sentences was difficult before but recently a paper named "A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SENTENCE EMBEDDINGS" was proposed which suggests a simple approach by computing the weighted average of word vectors in the sentence and then remove the projections of the average vectors on their first principal component.Here the weight of a word w is a/(a + p(w)) with a being a parameter and p(w) the (estimated) word frequency called smooth inverse frequency.this method performing significantly better.

A simple code to calculate the sentence vector using SIF(smooth inverse frequency) the method proposed in the paper has been given here

Step 3: using sklearn cosine_similarity load two vectors for the sentences and compute the similarity.

This is the most simple and efficient method to compute the sentence similarity.