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Weighting Common Syntactic Structures for Natural Language Based Information Retrieval

机译:基于自然语言的信息检索加权通用句法结构

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Natural Language Processing (NLP) techniques are believed to hold the potential to assist "bag-of-words" Information Retrieval (IR) in terms of retrieval accuracy. In this paper, we report a natural language based IR approach where the common syntactic structures between documents and the query is regarded to as a query-dependent feature for documents. Specifically, a "structural weight" is proposed for query terms, which can be seen as a weight to model the degree of term's involvement in the common syntactic structures. This structural weight is used together with the TF-IDF weighting scheme, which results in a new ranking function. The accumulation of this structural weight of all the query terms in the new ranking function will be seen as a measure of how much a document and a query share the common syntactic structures. The experimental results show that by using this ranking function, significant improvements in the retrieval performance are achieved.
机译:据信,自然语言处理(NLP)技术具有在检索准确性方面辅助“单词袋”信息检索(IR)的潜力。在本文中,我们报告了一种基于自然语言的IR方法,其中文档和查询之间的通用句法结构被视为文档的查询相关功能。具体来说,为查询术语提出了一种“结构权重”,可以将其视为对术语在常见句法结构中的参与程度进行建模的权重。该结构权重与TF-IDF加权方案一起使用,从而产生了新的排名函数。新排名功能中所有查询词的这种结构权重的累积将被视为对文档和查询共享共同语法结构的程度的度量。实验结果表明,通过使用该排序功能,可以显着提高检索性能。

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