Representing and manipulating context information is one of the hardest problems in natural language processing. This paper proposes a method for representing some context information so that the correct meaning for a word in a sentence can be selected. The approach is primarily based on work by Waltz and Pollack (1985, 1984), who emphasized neutrally plausible systems. By contrast this paper focuses on computationally feasible methods applicable to full-scale natural language processing systems.
There are two key elements: a collection of context vectors defined for every word used by a natural language processing system, and a context algorithm that computes a dynamic context vector at any position in a body of text. Once the dynamic context vector has been computed it is easy to choose among competing meanings for a word. This choice of definitions is essentially a neural network computation, and neural network learning algorithms should be able to improve such choices.
Although context vectors do not represent all context information, their use should improve those full-scale systems that have avoided context as being too difficult to deal with. Good candidates for full-scale context vector implementations are machine translation systems and Japanese word processors. A main goal of this paper is to encourage such large-scale implementations and tests of context vector approaches.
A variety of interesting directions for research in natural language processing and machine learning will be possible once a full set of context vectors has been created. In particular the development of more powerful context algorithms will be an important topic for future research.