Concatenation and product yield two new feature mappings, respectively: These new representations live in a much lower dimensional real spaces: The various segment-pair representations that we derived from pairs of head words can be generalized to the case in which we keep all the words in each segment.
a recent example of such approach is the Hellinger PCa embeddings of (Lebret and Collobert, 2014) which were built using Principal Component analysis based on Hellinger distance as dimensionality reduction algorithm.