Showing papers on "Natural language understanding published in 1978"
TL;DR: A tutorial survey of techniques for using contextual information in pattern recognition is presented, with emphasis on the problems of image classification and text recognition, where the text is in the form of machine and handprinted characters, cursive script, and speech.
119 citations
TL;DR: The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.
73 citations
DOI•
01 Mar 1978
TL;DR: It is shown that a comprehensive theory is emerging from research in computer vision, natural language understanding, cognitive psychology, and Artificial Intelligence programming language technology that composes new descriptions of sensory experience in terms of stored steriotypical knowledge of the world.
Abstract: This thesis is concerned with aspects of a theory of machine perception. It is shown that a comprehensive theory is emerging from research in computer vision, natural language understanding, cognitive psychology, and Artificial Intelligence programming language technology. A number of aspects of machine perception are characterized. Perception is a recognition process which composes new descriptions of sensory experience in terms of stored steriotypical knowledge of the world. Perception requires both a schema-based formalism for the representation of knowledge and a model of the processes necessary for performing search and deduction on that representation. As an approach towards the development of a theory of machine perception, a computational model of recognition is presented. The similarity of the model to formal mechanisms in parsing theory is discussed. The recognition model integrates top-down, hypothesis-driven search with bottom-up, data-driven search in hierarchical schemata representations. Heuristic procedural methods are associated with particular schemata as models to guide their recognition. Multiple methods may be applied concurrently in both top-down and botton-up search modes. The implementation of the recognition model as an Artificial Intelligence programming language called MAYA is described. MAYA is a multiprocessing dialect of LISP that provides data structures for representing schemata networks and control structures for integrating top-down and bottom-up processing. A characteristic example from scene analysis, written in MAYA, is presented to illustrate the operation of the model and the utility of the programming language. A programming reference manual for MAYA is included. Finally, applications for both the recognition model and MAYA are discussed and some promising directions for future research proposed.
15 citations
01 Sep 1978
TL;DR: A detailed description--plus all the LISP code--of a miniature versions of well-known natural language understanding programs; SAM, Script Applying Mechanism, and ELI, English Language Interpreter, intended to be simple enough so that interested computer scientists and psychologists can understand, modify, and extend programs dealing with knowledge structure based understanding.
Abstract: : This report contains a detailed description--plus all the LISP code--of a miniature versions of well-known natural language understanding programs; SAM, Script Applying Mechanism, and ELI, English Language Interpreter. The programs are intended to be simple enough so that interested computer scientists and psychologists, with only an introductory knowledge of LISP, can understand, modify, and extend programs dealing with knowledge structure based understanding. (Author)
2 citations
TL;DR: A program that understands Natural Language Commands, i.e. which have a free form but a well-defined semantic content, is presented and it is shown that by Structural Pattern Recognition techniques, the program “learns” the vocabulary and the structure of the sentences.
1 citations
18 Jul 1978
TL;DR: A simple notation for talking about the goals of the participants is introduced and it is suggested that given a sufficiently precise semantics, this notation could efficiently describe the knowledge needed by a system which could use, and understand the use by others, of a small subset of social actions.
Abstract: What is a social action, and what would it be for a social actions to be incorporated into a natural language understanding system? A simple notation for talking about the goals of the participants is introduced and it is suggested that given a sufficiently precise semantics, this notation could efficiently describe the knowledge needed by a system which could use, and understand the use by others, of a small subset of social actions.
1 citations