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Chapter 18 Rough Set Based Concept Extraction Paradigm for Document Ranking

机译：第18章基于粗糙集的概念提取范式用于文档排名

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摘要

On the World Wide Web, Open domain Question Answering System is one of the emerging information retrieval systems which are becoming popular day by 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 of the major tasks in the representation of retrieved results and directly contributes towards accuracy of a retrieval system. Rough sets are widely used for document categorization, vocabulary reduction, and other information retrieval problems. We are proposing a computationally efficient rough set based method for ranking of the documents. The distinctive point of the proposed algorithm is to give more emphasis on 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 our method with online Question Answering System AnswerBus and observed 38% improvement in ranking relevant documents on top ranks. We conducted more experiments to judge the effectiveness of the information retrieval system and found satisfactory performance results.
机译：在万维网上，开放域问答系统是新兴的信息检索系统之一，为了响应用户的问题而获得简洁相关的答案，该系统日益流行。在本文中，我们正在解决基于粗糙集的文档排名方法，这是表示检索结果的主要任务之一，直接有助于检索系统的准确性。粗糙集被广泛用于文档分类，词汇减少和其他信息检索问题。我们正在提出一种基于计算效率的基于粗糙集的文档排名方法。所提出算法的独特之处在于将更多重点放在概念组合的存在和位置上，而不是术语频率上。我们已经使用Google和我们提出的方法对从TREC，Wordbook，WorldFactBook收集的一系列标准问题进行了实验。我们发现文档排名性能提高了16％。此外，我们将我们的方法与在线问答系统AnswerBus进行了比较，并发现将相关文档的排名提高了38％。我们进行了更多的实验来判断信息检索系统的有效性，并找到令人满意的性能结果。

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