A network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning rep?resentations to novel sentences (both simple and embedded) which contain words in syntactic positions they did not oc?cupy during training. Herein we describe a network which displays strong semcintic systematicity in response to unsu?pervised tiaimng. During tradning, two-thirds of all nouns are presented only in a single syntactic position (either as gram?matical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levek of embedding. Successful training requires a corpus of about 1000 sentences, and network training is quite rapid.