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Rough Set Based Concept Extraction Paradigmfor Document Ranking

机译：基于粗糙集的概念提取帕拉德文档排名

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摘要

On the World Wide Web, Open domain Question Answering System isone of the emerging information retrieval systems which are becoming popular dayby day to get succinct relevant answers in response of users' questions. In this paper,we are addressing rough set based method for document ranking which is one ofthe major tasks in the representation of retrieved results and directly contributestowards accuracy of a retrieval system. Rough sets are widely used for documentcategorization, vocabulary reduction, and other information retrieval problems. Weare proposing a computationally efficient rough set based method for ranking of thedocuments. The distinctive point of the proposed algorithm is to give more emphasison presence and position of the concept combination instead of term frequencies.We have experimented over a set of standard questions collected from TREC,Wordbook, WorldFactBook using Google and our proposed method. We found 16%improvement in document ranking performance. Further, we have compared ourmethod with online Question Answering System AnswerBus and observed 38%improvement in ranking relevant documents on top ranks. We conducted more ex-periments to judge the effectiveness of the information retrieval system and foundsatisfactory performance results.
机译：在万维网上，开放式域问题回答系统的新兴信息检索系统，这是经过深受日子的日子，以获得对用户问题的响应的简洁相关答案。在本文中，我们正在寻址基于粗糙的文件排名方法，这是检索结果表示的主要任务之一，并直接提供检索系统的准确性。粗糙集广泛用于DocumentCategorization，词汇减少和其他信息检索问题。佩戴提出基于计算的计算高效粗糙集的测量方法。所提出的算法的独特点是提供更加强调的存在和概念组合的位置而不是术语频率。我们已经尝试了使用Google和我们提出的方法从TREC，Wordbook，WorldFactBook收集的一组标准问题。我们发现文档排名绩效的提高了16％。此外，我们已经将OverMethod与在线问题接听系统答案中的答案中进行了比较，并观察到在最高级别的排名相关文件中的38％。我们对判断信息检索系统的有效性和发现的绩效结果进行了更多的前提。

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