A localist connectionist network for language processing has nodes for words, phrase types, and concepts. When a sentence is presented to the system, nodes corresponding to the words are activated. Activation spreads through the system, and it eventually settles on a pattern of activation across a set of nodes representing an interpretation. Distributed connectionist approaches Connectionist networks may learn the mapping between distributed representations of form and meaning or written language and pronunciation. Language tends to be ambiguous .
Language tends to be vague .
Language may make use of metaphor and metonymy. People understand it even when it's ill-formed or contains unfamiliar words. Language generation requires planning based on mutual knowledge. One form in one language may correspond to many in another, and the selection rule may be very complex, requiring a relatively deep understanding of the original.