Interest-based Information Filtering and Extraction in Natural Language Understanding Systems

Given the vast amount of information available to the average person, there is a growing need for mechanisms that can select relevant or useful information based on some specification of the interests of a user. Furthermore, experience with natural language understanding and reasoning programs in artificial intelligence has demonstrated that the combinatorial explosion of possible conclusions that can be drawn from any input is a serious computational bottleneck in the design of computer programs that process information automatically.

This paper presents a theory of interestingness that serves as the basis for two story understanding programs, one that can filter and extract information likely to be relevant or interesting to a user, and another that can formulate and pursue its own interests based on an analysis of the information necessary to carry out the tasks it is pursuing. We discuss the basis for our theory of interestingness, heuristics for interest-based processing of information, and the process used to filter and extract relevant information from the input.

Read the paper:

Interest-based Information Filtering and Extraction in Natural Language Understanding Systems

by Ashwin Ram

Bellcore Workshop on High-Performance Information Filtering, Morristown, NJ, November 1991

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