We utilize m convolution filters to extract the relevance signals for each word based on its local window of size l in the document.
after calculating the interaction-based representation d = (e c 1 , e c 2 , ..., e c |d| ) for document d and category c, we apply the convolution operation as follows: where r i ∈ R m is the hidden features regarding the relevance signal extracted for i-th word, W 1 ∈ R m×3le(2l+1) and b 1 ∈ R m are the weight matrix and the corresponding bias vector respectively, e c i−l:i+l refers to the concatenation from e c i−l to e c i+l .