<p>I have tried the methods provided by the previous answers. It works, but the main drawback of it is that the longer the sentences the larger similarity will be(to calculate the similarity I use the cosine score of the two mean embeddings of any two sentences) since the more the words the more positive semantic effects will be added to the sentence. </p>

<p>I thought I should change my mind and use the sentence embedding instead as studied in <a href="https://arxiv.org/abs/1502.06922" rel="nofollow noreferrer">this paper</a> and <a href="http://aclweb.org/anthology/W16-1628" rel="nofollow noreferrer">this</a>. </p>
How to calculate the sentence similarity using word2vec model of gensim with python
<p>According to the <a href="http://radimrehurek.com/gensim/models/word2vec.html" rel="noreferrer">Gensim Word2Vec</a>, I can use the word2vec model in gensim package to calculate the similarity between 2 words.</p>

<p>e.g.</p>

<pre><code>trained_model.similarity('woman', 'man') 
0.73723527
</code></pre>

<p>However, the word2vec model fails to predict the sentence similarity. I find out the LSI model with sentence similarity in gensim, but, which doesn't seem that can be combined with word2vec model. The length of corpus of each sentence I have is not very long (shorter than 10 words).  So, are there any simple ways to achieve the goal?</p>