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    • 7. 发明申请
    • COST-BENEFIT APPROACH TO AUTOMATICALLY COMPOSING ANSWERS TO QUESTIONS BY EXTRACTING INFORMATION FROM LARGE UNSTRUCTURED CORPORA
    • 成本效益自动组合方式从大型非结构性公司提取信息提出问题
    • US20090192966A1
    • 2009-07-30
    • US12417959
    • 2009-04-03
    • Eric J. HorvitzDavid R. AzariSusan T. DumaisEric D. Brill
    • Eric J. HorvitzDavid R. AzariSusan T. DumaisEric D. Brill
    • G06N5/02
    • G06F17/30684G06F17/30687Y10S707/99933
    • The present invention relates to a system and methodology to facilitate extraction of information from a large unstructured corpora such as from the World Wide Web and/or other unstructured sources. Information in the form of answers to questions can be automatically composed from such sources via probabilistic models and cost-benefit analyses to guide resource-intensive information-extraction procedures employed by a knowledge-based question answering system. The analyses can leverage predictions of the ultimate quality of answers generated by the system provided by Bayesian or other statistical models. Such predictions, when coupled with a utility model can provide the system with the ability to make decisions about the number of queries issued to a search engine (or engines), given the cost of queries and the expected value of query results in refining an ultimate answer. Given a preference model, information extraction actions can be taken with the highest expected utility. In this manner, the accuracy of answers to questions can be balanced with the cost of information extraction and analysis to compose the answers.
    • 本发明涉及一种便利从诸如万维网和/或其他非结构化来源的大型非结构化语料库提取信息的系统和方法。 通过概率模型和成本效益分析,可以通过这些来源自动构成问题答案形式的信息,以指导基于知识的问答系统采用的资源密集型信息提取程序。 分析可以利用由贝叶斯或其他统计模型提供的系统生成的答案的最终质量的预测。 当与实用新型相结合时,这种预测可以为系统提供对发出给搜索引擎(或引擎)的查询数量的决定的能力,考虑到查询的成本和查询结果的期望值来提炼最终的 回答。 给定一个偏好模型,可以采用最高预期效用的信息提取动作。 以这种方式,可以将问题答案的准确性与信息提取和分析的成本进行平衡,以构成答案。