1. A method, implemented by one or more computing devices, for processing linguistic items, comprising: receiving a word sequence formed by a sequence of words; forming a plurality of window vectors, each representing a set of z consecutive words in the word sequence; transforming the window vectors into a plurality of local contextual feature (LCF) vectors, based on a first part of a convolutional latent semantic model; generating a global feature vector by selecting, for each dimension of the LCF vectors, a maximum value specified by the LCF vectors, with respect to that dimension; and projecting the global feature vector into a concept vector, based on a second part of the convolutional latent semantic model, the convolutional latent semantic model being trained based on click-through data.