SELF-ORGANIZING WORD REPRESENTATIONS FOR FAST SENTENCE PROCESSING

Several psycholinguistic models represent words as vectors in a high-dimensional state space, such that distances between vectors encode the strengths of paradigmatic relations between the represented words. This chap-ter argues that such an organization develops because it facilitates fast sentence processing. A model is presented in which sentences, in the form of word-vector sequences, serve as input to a recurrent neural network that provides random dynamics. The word vectors are adjusted by a process of self-organization, aimed at reducing fluctuations in the dynamics. As it turns out, the resulting word vectors are organized paradigmatically