共查询到19条相似文献，搜索用时 156 毫秒
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《内蒙古科技与经济》2017,(17)
以中国知网全文数据库2006年~2016年间、图书情报领域内信息检索相关性的文献为研究对象,运用文献计量的方法与可视化研究工具,旨在梳理国内图情领域近十年信息检索相关性的学术脉络,为进一步深入研究提供参考。 相似文献
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以CNKI数字出版平台所收录的文献为依据,对近年来我国跨语言信息检索(CLIR:Cross Language Information Retrieval)的文献进行文献计量学统计,选取2001-2012年间研究成果作为数据样本,并从文献年代分布、文献被引情况、文献情报源分布、研究人员及机构分布、获得资助情况、关键词及论文主题分布进行统计分析,对2001-2012年关于跨语言信息检索的研究现状进行了梳理和总结,从而为进一步的研究和发展提供参考。 相似文献
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1998～2003年我国网络信息检索研究论文的定量分析与研究 总被引：2，自引：1，他引：2
本文以《中国期刊全文数据库》为统计工具，采用文献计量方法对1998—2003年我国期刊中发表的关于网络信息检索的316篇重要研究论文进行时间分布、主题内容分布、期刊分布和著者分布分析，探讨网络信息检索在我国的发展情况，展望网络信息检索研究的未来发展趋势。 相似文献
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可视化信息检索研究文献的量化可视分析 总被引：1，自引：0，他引：1
文章利用当前最著名的可视化分析软件CitespaceⅡ对从SCI与SSCI中下载的1989-2010年间可视化信息检索研究的全部文献做了详细的可视化分析,描述了可视化信息检索研究的发展轨迹,揭示了其研究热点的变化情况,分析了此项研究的国家与研究机构分布情况。 相似文献
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相关性基础理论及其在检索建模中的作用研究 总被引：1，自引：0，他引：1
本文是对信息检索的一次理论研究。在总结了已有相关性研究的基础上,对信息检索模型之中的相关性因素做了系统梳理,认为现有信息检索模型中的系统相关性因素体现得不十分明显,同时用户相关性的因素没有很好地纳入系统相关性和系统设计研究之中。与相关性有关的概念是相似,它存在于文本空间之中。与相关相比,相似具有更好的数学特征。查询是相关判断的过程载体,它同时也是信息检索研究的瓶颈。寻找更为恰当的相关性的隐喻则需要跳出文本的藩篱,从更为深入的模式相关切入,探索更为复杂的相关性因素。 相似文献
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[目的/意义]旨在为信息检索相关性研究提供参考。[方法/过程]以CNKI为数据源,采用定性方法,从信息检索的历史脉络和研究学派进行梳理总结,分析信息检索的影响因素和发展趋势。[结果/结论]信息检索相关性是用户、系统的相关性的综合体,任何一方都不能脱离。相关性应该是以用户为关键,系统为基础,研究用户与检索系统的交互、认知以及真实需求的描述与反馈。随着信息检索相关性研究的深入,系统观与用户观将会相互交融,检索技术与用户需求将会协调统一,共同推进检索相关性的发展。 相似文献
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董惟昕 《内蒙古科技与经济》2015,(6):100-101,104
重点介绍了基于Internet环境下的化学化工文献信息检索的特点、方法及利用途径,依托于因特网为用户提供的信息检索和服务的网站系统,对文献信息来源、类型以及文献信息检索的特点、方法进行了较深入的研究,以期让网络环境下的化学化工文献资源和服务可以得到更充分的利用。 相似文献
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目前大多数机器翻译和跨语言检索系统都是基于通用语料,对外文科技资料的翻译效果不理想,本文结合科技文献的加工方法,研究面向科技文献的跨语言信息检索系统的模型。首先对跨语言信息检索的概念和特点进行简单的概述,从3个角度介绍跨语言信息检索的研究方法,然后讨论构建跨语言信息检索系统的必要性,在此基础上设计出一个面向科技文献的跨语言信息检索系统模型以及主要功能结构。 相似文献
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相关性是衡量信息检索系统性能的重要指标之一,对信息检索相关性进行深入分析对于信息检索系统的有效开发具有重要的理论指导意义.从系统、用户以及当前的网络环境信息检索三个方面分析了信息检索相关性的评价方法及改善策略. 相似文献
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《Information processing & management》2005,41(4):1003-1018
The authors introduce an information visualization model, WebStar, for hyperlink-based information systems. Hyperlinks within a hyperlink-based document can be visualized in a two-dimensional visual space. All links are projected within a display sphere in the visual space. The relationship between a specified central document and its hyperlinked documents is visually presented in the visual space. In addition, users are able to define a group of subjects and to observe relevance between each subject and all hyperlinked documents via movement of that subject around the display sphere center. WebStar allows users to dynamically change an interest center during navigation. A retrieval mechanism is developed to control retrieved results in the visual space. Impact of movement of a subject on the visual document distribution is analyzed. An ambiguity problem caused by projection is discussed. Potential applications of this visualization model in information retrieval are included. Future research directions on the topic are addressed. 相似文献
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In this paper, we propose a document reranking method for Chinese information retrieval. The method is based on a term weighting scheme, which integrates local and global distribution of terms as well as document frequency, document positions and term length. The weight scheme allows randomly setting a larger portion of the retrieved documents as relevance feedback, and lifts off the worry that very fewer relevant documents appear in top retrieved documents. It also helps to improve the performance of maximal marginal relevance (MMR) in document reranking. The method was evaluated by MAP (mean average precision), a recall-oriented measure. Significance tests showed that our method can get significant improvement against standard baselines, and outperform relevant methods consistently. 相似文献
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《Information processing & management》1999,35(2):121-139
It is well-known that relevance feedback is a method significant in improving the effectiveness of information retrieval systems. Improving effectiveness is important since these information retrieval systems must gain access to large document collections distributed over different distant sites. As a consequence, efforts to retrieve relevant documents have become significantly greater. Relevance feedback can be viewed as an aid to the information retrieval task. In this paper, a relevance feedback strategy is presented. The strategy is based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. This paper describes a neural information retrieval model and emphasizes the results obtained with the associated relevance back-propagation algorithm in three different environments: manual ad hoc, automatic ad hoc and mixed ad hoc strategy (automatic plus manual ad hoc). 相似文献
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How to merge and organise query results retrieved from different resources is one of the key issues in distributed information retrieval. Some previous research and experiments suggest that cluster-based document browsing is more effective than a single merged list. Cluster-based retrieval results presentation is based on the cluster hypothesis, which states that documents that cluster together have a similar relevance to a given query. However, while this hypothesis has been demonstrated to hold in classical information retrieval environments, it has never been fully tested in heterogeneous distributed information retrieval environments. Heterogeneous document representations, the presence of document duplicates, and disparate qualities of retrieval results, are major features of an heterogeneous distributed information retrieval environment that might disrupt the effectiveness of the cluster hypothesis. In this paper we report on an experimental investigation into the validity and effectiveness of the cluster hypothesis in highly heterogeneous distributed information retrieval environments. The results show that although clustering is affected by different retrieval results representations and quality, the cluster hypothesis still holds and that generating hierarchical clusters in highly heterogeneous distributed information retrieval environments is still a very effective way of presenting retrieval results to users. 相似文献
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Considerable evidence exists to show that the use of term relevance weights is beneficial in interactive information retrieval. Various term weighting systems are reviewed. An experiment is then described in which information retrieval users are asked to rank query terms in decreasing order of presumed importance prior to actual search and retrieval. The experimental design is examined, and various relevance ranking systems are evaluated, including fully automatic systems based on inverse document frequency parameters, human rankings performed by the user population, and combinations of the two. 相似文献
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This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance decisions, and it is a hybrid of various existing effective models and techniques. It estimates the relevance decision preference of a document context as the log-odds and uses smoothing techniques as found in language models to solve the problem of zero probabilities. It combines these estimated preferences of document contexts using different types of aggregation operators that comply with different relevance decision principles (e.g., aggregate relevance principle). Our model is evaluated using retrospective experiments (i.e., with full relevance information), because such experiments can (a) reveal the potential of our model, (b) isolate the problems of the model from those of the parameter estimation, (c) provide information about the major factors affecting the retrieval effectiveness of the model, and (d) show that whether the model obeys the probability ranking principle. Our model is promising as its mean average precision is 60–80% in our experiments using different TREC ad hoc English collections and the NTCIR-5 ad hoc Chinese collection. Our experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents. 相似文献
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Niall Rooney David Patterson Mykola Galushka Vladimir Dobrynin 《Information processing & management》2006
Contextual document clustering is a novel approach which uses information theoretic measures to cluster semantically related documents bound together by an implicit set of concepts or themes of narrow specificity. It facilitates cluster-based retrieval by assessing the similarity between a query and the cluster themes’ probability distribution. In this paper, we assess a relevance feedback mechanism, based on query refinement, that modifies the query’s probability distribution using a small number of documents that have been judged relevant to the query. We demonstrate that by providing only one relevance judgment, a performance improvement of 33% was obtained. 相似文献
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