Word sense disambiguation by a higher order connectionist net based on distributed representations

Word sense disambiguation is one of the most challenging areas in natural language processing. We describe a higher order neural net based on a distributed representation method for word sense disambiguation. Two types of theory of neural representations, which are localist and distributed representations, are discussed and compared. The concept of microfeatures which typically belongs to the category of distributed representation is introduced and incorporated in the system. We show that a system based on a distributed representation is potentially more effective than that based on a localist representation in solving the word sense disambiguation problem.<>