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Identification of Navigation Lead Candidates Using Citation and Co-Citation Analysis

机译:利用引文和共引分析法识别领航候选人

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Query refinement is an integral part of search, especially for the exploratory search scenarios, which assume that the users start with ill-defined information needs that change over time. In order to support exploratory search and navigation, we have proposed an approach of exploratory navigation in digital libraries using navigation leads. In this paper, we focus specifically on the identification of the navigation lead candidates using keyword extraction. For this purpose, we utilize the citation sentences as well as the co-citations. We hypothesize that they can improve the quality of the extracted keywords in terms of finding new keywords (that would not be otherwise discovered) as well as promoting the important keywords by increasing their relevance. We have quantitatively evaluated our method in the domain of digital libraries using experts' judgement on the relevance of the extracted keywords. Based on our results, we can conclude that using the citations and the co-citations improves the results of extraction of the most relevant terms over the TF-IDF baseline.
机译:查询优化是搜索不可或缺的一部分,尤其是对于探索性搜索场景而言,这种假设假设用户开始时使用的定义不明确的信息需求会随着时间的推移而变化。为了支持探索性搜索和导航,我们提出了使用导航线索在数字图书馆中进行探索性导航的方法。在本文中,我们专门针对使用关键字提取来识别导航线索候选者。为此,我们利用了引文句子和共同引文。我们假设它们可以通过查找新的关键字(否则将不会发现)来提高提取的关键字的质量,并可以通过增加其相关性来促进重要的关键字。我们使用专家对所提取关键字的相关性的判断,对数字图书馆领域中的方法进行了定量评估。根据我们的结果,我们可以得出结论,使用引文和共同引文可以改善TF-IDF基线上最相关术语的提取结果。

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