A Network for Encoding,Decoding and Translating Locative Prepositions

In general, the spatial relationship between two objects can be roughly expressed by a locative expression of the form ‘noun-preposition-noun’. The mapping from preposition to meaning is context sensitive; for example, the preposition ‘on’ expresses different spatial relationships in the phrases ‘house on lake’ and ‘plate on table’. While very accurate selection of the proper sense of a preposition depends on the broad context, the immediate context (the two nouns) can often lend enough information to make a reasonable judgement. Back-propagation is used to train a feed-forward network to associate locative prepositions with semantic representations using several contexts (noun pairs). After training, the network can produce a core meaning for each preposition; this prototype meaning can be altered by the influence of the nouns, even for pairs which are not included in the training set. A toy example of machine translation of prepositions is presented in which two networks are trained, one using English prepositions, the other using German prepositions. After successful training of the two networks, attempts were made at translating from one language to the other.