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Integrating case-based learning and cognitive biases for machine learning of natural language

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This paper shows that psychological constraints on human information processing can be used effectively to guide feature set selection for case-based learning of linguistic knowledge. Given as input a baseline case representation for a natural language learning task, our algorithm selects the relevant cognitive biases for the task and then automatically modifies the representation in response to those biases by changing, deleting, and weighting features appropriately. We apply the cognitive bias approach to feature set selection to four natural language learning problems and show that performance of the case-based learning algorithm improves significantly when relevant cognitive biases are incorporated into the baseline instance representation. We argue that the cognitive bias approach offers new possibilities for case-based learning of natural language: it simplifies the process of instance representation design and, in theory, obviates the need for separate instance representations for each linguistic knowledge acquisition task. More importantly, the approach offers a mechanism for explicitly combining the frequency information available from corpus-based techniques with cognitively-based preferences employed in traditional linguistic and knowledge-based approaches to natural language processing.
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Keywords: CASE-BASED LEARNING; DISAMBIGUATION; FEATURE WEIGHTING; LEXICAL TAGGING; RELATIVE PRONOUN

Document Type: Research Article

Publication date: July 1, 1999

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