The temporal context model was developed to describe episodically-formed associations between words presented in temporal proximity. By allowing words to retrieve and update the previous contexts in which they were presented, the model can learn higher-order co-occurrence information as well. We extend the model to form stable representations that capture latent relations between presented words. We test the model on artificial texts with simple known generating structures, as well as naturally-occurring text. In particular, we study semantic information learned from the TASA corpus, where the model's performance is comparable to that of LSA on standard synonym tests. Taken with the temporal context model's ability to describe episodic association, this suggests that contextual encoding and retrieval are fundamental computations common to episodic and semantic memory. |