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Semantic Indexing For Question Answering System

机译:问答系统的语义索引

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With the vast growth of various forms of digital data, automated indexing has become very important so that it enables the needs of the current users to be fulfilled. Keywords based indexing has failed to accommodate to the needs of the present demands. The representation of the document content as well as the indexing process is a crucial factor that ensures the success of retrieval process. Therefore, this research introduces a new approach in creating semantic indexing that uses Skolem representation which automatically indexes multiple documents into a single knowledge representation. This knowledge representation will then be used by the proposed question answering system in retrieving the answers as well as pointing to the documents the answer contains based on the user query. The system managed to achieve 93.84% of recall and 82.92% of precision.
机译:随着各种形式的数字数据的迅猛增长,自动索引已变得非常重要,因此它可以满足当前用户的需求。基于关键字的索引无法适应当前需求。文档内容的表示以及索引过程是确保检索过程成功的关键因素。因此,本研究引入了一种新的创建语义索引的方法,该方法使用Skolem表示将自动将多个文档编入单个知识表示。然后,所提出的问题回答系统将使用该知识表示来检索答案以及根据用户查询指向答案包含的文档。该系统设法实现93.84%的召回率和82.92%的精度。

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