Combining Symbolic and Distributional Models of Meaning

A method is to be adapted from the Cognitive Science literature, in which symbolic and connectionist representations are combined using tensor products, to adapt a method for language processing.

Abstract

The are two main approaches to the representation of meaning in Computational Linguistics: a symbolic approach and a distributional approach. This paper considers the fundamental question of how these approaches might be combined. The proposal is to adapt a method from the Cognitive Science literature, in which symbolic and connectionist representations are combined using tensor products. Possible applications of this method for language processing are described. Finally, a potentially fruitful link between Quantum Mechanics, Computational Linguistics, and other related areas such as Information Retrieval and Machine Learning, is proposed.