| Knowledge Extraction from Text: Machine Learning for Text-to-rule Translation (1993) | |||||||||||||||
Abstract | |||||||||||||||
| Learning from texts is a noble but distant research goal. We investigate the practicality of a more modest enterprise in which machine learning (ML) and natural language processing (NLP) would mutually reinforce each other. We are designing a system that will process technical expository texts, in which narratives (embodying general knowledge) and examples (specific knowledge) are interleaved to best instruct the reader. A broad-coverage parser of English technical texts and an interactive case analyzer provide the front end. Their output will be translated into Horn clauses; some user participation will be required throughout the process. We discuss how the application of absorption will provide a useful hierarchization of the domain theory. We then apply the EBL approach, using translation of the narratives as the domain theory, and translation of the examples as the training examples. We illustrate with examples that such learning may result in a reformulation... | |||||||||||||||
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