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MACHINE-LEARNED APPROACH TO DETERMINING DOCUMENT RELEVANCE FOR SEARCH OVER LARGE ELECTRONIC COLLECTIONS OF DOCUMENTS
MACHINE-LEARNED APPROACH TO DETERMINING DOCUMENT RELEVANCE FOR SEARCH OVER LARGE ELECTRONIC COLLECTIONS OF DOCUMENTS
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机译:确定较大电子文档集合中文档相关性的机器学习方法
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摘要
The present invention relates to a system and methodology that applies automated learning procedures for determining document relevance and assisting information retrieval activities. A system is provided that facilitates a machine-learned approach to determine document relevance. The system includes a storage component that receives a set of human selected items to be employed as positive test cases of highly relevant documents. A training component trains at least one classifier with the human selected items as positive test cases and one or more other items as negative test cases in order to provide a query-independent model, wherein the other items can be selected by a statistical search, for example. Also, the trained classifier can be employed to aid an individual in identifying and selecting new positive cases or utilized to filter or re-rank results from a statistical-based search.
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