Vincent E. Giuliano


Word and context association by means of linear networks
Vincent E. Giuliano
Proceedings of the Annual meeting of the Association for Machine Translation and Computational Linguistics

This paper is concerned with the use of electrical networks for the automatic recognition of statistical associations among words and contexts present in written text. A general mathematical theory is proposed for association by means of linear transformations, and it is shown that this theory can be realized through use of passive linear electrical networks. Several smallscale experimental associative networks have been built, and are briefly described in the paper; one such device will be demonstrated in the course of the oral presentation of the paper. Some of the devices generate measures of association among index terms used to characterize a document collection, and between the index terms and the documents themselves. Another uses syntactic proximity within sentences as a criterion for the generation of word association measures. Examples are given of associations produced by these network devices. It is conjectured that the networkproduced association measures reflect two distinct types of linguistic association—“synonymy” association which reflects similarity of meaning, and “contiguity” association which reflects real-world relationships among designata.