Learning Semantic Relationships in Compound Nouns with Connectionist Networks

This paper describes a new approach for understanding compound nouns. Since several approaches have demonstrated the difficulties in finding detailed and suitable semantic relationships within compound nouns, we use only a few basic semantic relationships and provide the system with the additional ability to learn the details of these basic semantic relationships from training examples. Our system is based on a backpropagation architecture and has been trained to understand compound nouns from a scientific technical domain. The test results demonstrated that a connectionist network is able to learn semantic relationships within compound nouns.