A parallel distributed processing (PDP) model of resolution of ambiguous Japanese nouns and verbs shown with contexts is presented. As a result of phonological restriction, Japanese possesses a large number of homophones. In understanding Japanese, the problem of lexical ambiguity resolution is especially important. In implementing the PDP model, the pronunciation, spelling, syntactic information, and semantic information of ambiguous words are represented as a distributed pattern of activation levels over a large set of simple processing units in a fully interconnected network. After the network is trained using the delta rule, the recall of learned patterns is evaluated by presenting just part of the lexical and contextual information. The similarity of recalled and learned patterns is used to indicate lexical decision times or the frequency of recall in empirical results. Consistent with several experimental data, the PDP model can successfully simulate the effect of context-dependent and context-independent frequencies of ambiguous nouns and verbs in the time-course of activation, as well as accessing an alternative meaning of ambiguous nouns in associative recall.