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Research and Advances

High level knowledge sources in usable speech recognition systems

The authors detail an integrated system which combines natural language processing with speech understanding in the context of a problem solving dialogue. The MINDS system uses a variety of pragmatic knowledge sources to dynamically generate expectations of what a user is likely to say.
Research and Advances

Spreadsheet analysis and design

Although spreadsheet programs and microcomputers have revolutionized information processing in organizations, a significant number of serious errors have been reported through the misuse of this technology. This article discusses several different contexts for the development of spreadsheet models and presents structured design techniques for these models.
Research and Advances

The use of memory in text processing

The performance of a natural language processing system should improve as it reads more and more texts. This is true both for systems intended as cognitive models and for practical text processing systems. Permanent long-term memory should be useful during all stages of text understanding. For example, if, while reading a patent abstract about a new disk drive, a system can retrieve information about similar objects from memory, processing should be simplified. However, most natural language programs do not exhibit such learning behavior. We describe in this article how RESEARCHER, a program that reads, remembers and generalizes from patent abstracts, makes use of its automatically generated memory to assist in low-level text processing, primarily involving disambiguation that could be accomplished no other way. We describe both RESEARCHER's basic understanding methods and the integration of memory access. Included is an extended example of RESEARCHER processing a patent abstract by using information about several other abstracts already in memory.
Research and Advances

Experimental investigation of an interior search method within a simplex framework

A feasible direction method for solving Linear Programming (LP) problems, followed by a procedure for purifying a non-basic solution to an improved extreme point solution have been embedded within an otherwise simplex based optimizer. The algorithm is designed to be hybrid in nature and exploits many aspects of sparse matrix and revised simplex technology. The interior search step terminates at a boundary point which is usually non-basic. This is followed by a series of minor pivotal steps which lead to a basic feasible solution with a superior objective function value. It is concluded that the procedures discussed in this article are likely to have three possible applications, which are (i) improving a non-basic feasible solution to a superior extreme point solution, (ii) an improved starting point for the revised simplex method, and (iii) an efficient implementation of the multiple price strategy of the revised simplex method.

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