It takes two n-element vectors as input and compresses semantics of the two vectors into one n-element vector in hidden layer by, where [x 1 ; x 2 ] is the concatenation of the two input vectors and f is the sigmoid function; W (dep) is a parameter matrix that is chosen according to the dependency relation between x 1 and x 2 (In the case of y 1 , W (dep) = W (xcomp) ), which is initialized by I i , where I i is a n × n unit matrix, I b is a n-element null vector, and is sampled from a uniform distribution U [−0.001, 0.001] (Socher et al., 2013).
we combine embedding vectors of words in a linking relation by a recursive autoencoder (Socher et al., 2011) according to syntactic dependency structure.