We use a convolutional network (CNN) to encode Q into a d-dimensional vector e. For candidate topic entity C i , we encode three types of KB aspects, namely, the entity name, entity type and surrounding relations where both entity name and type are represented as a sequence of words while surrounding relations are represented as a bag of sequences of words.
we use three CNNs to encode them into three d-dimensional vectors, namely, C n i , C t i and C r 1 i .