# What is the right way to convolve over word embeddings?

I have two word embeddings $$w_1$$ and $$w_2$$ with dimension 100 as input to a convolutional neural network. It should learn the similarity between these two words.

I am now concerned with the applied convolution operation. What is a reasonable filter size? In which way should the convolution operation perform on the two word embeddings?