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Measuring Textual Context Based on Cognitive Principles
Abstract
Based on the principle of cognitive economy, the complexity and the information of textual context are proposed to measure subjective cognitive degree of textual context. Based on minimization of Boolean complexity in human concept learning, the complexity and the difficulty of textual context are defined in order to mimic human’s reading experience. Based on maximal relevance principle, the information and cognitive degree of textual context are defined in order to mimic human’s cognitive sense. Experiments verify that more contexts are added, more easily the text is understood by a machine, which is consistent with the linguistic viewpoint that context can help to understand a text; furthermore, experiments verify that the author-given sentence sequence includes the less complexity and the more information than other sentence combinations, that is to say, author-given sentence sequence is more easily understood by a machine. So the principles of simplicity and maximal relevance actually exist in text writing process, which is consistent with the cognitive science viewpoint. Therefore, this chapter’s measuring methods are validated from the linguistic and cognitive perspectives, and it could provide a theoretical foundation for machine-based text understanding.
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