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信息检索系统中的相关性评价问题 总被引：1，自引：1，他引：1
侯震宇 《现代图书情报技术》2003,19(2):45-47
从理论上论述了基于关键字和基于链接的检索模型是如何进行待检索文章的相关性评价，并讨论了各种模型的优点和一些不足。 相似文献
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如何有效的进行生物医学文献检索和信息挖掘,是计算机技术和生物信息技术研究领域中的一个经典课题。本文对生物医学文献中自然语言问题文档,片段,概念和RDF三元组,构建了高效的检索和问答系统。特别的,在文档检索中,我们搭建了基于顺序依赖模型,词向量,和伪相关反馈相结合的通用检索模型;同时,前k个文档被分离为句子和片段,并以此建立检索索引,并基于文档检索模型,完成片段检索;在概念挖掘中,提取生物医学的概念,列出相关的概念属于网络服务的五个数据库链接,通过得分排名得到最终的概念。在CLEF BioASQ几年的评测数据上,我们构造的检索系统都取得了不错的性能。 相似文献
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Web环境下的个性化信息检索技术 总被引：5，自引：0，他引：5
对当前网络环境下应用的若干典型的个性化信息检索技术进行了研究和分析。介绍了个性化信息检索的一般模型，从四个不同的角度对各种个性化检索技术做了分类，列举了主要的个性化检索个案，描述了个性化检索系统的核心模块，并简单介绍了如何提高个性化检索的效率。 相似文献
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当今的科技信息发展环境中,信息检索用户的认知行为和需求心理极为复杂.如何将信息检索中用户的潜意识显现出来,以便更好地服务于检索系统建设,是信息检索研究中的核心难题之一.研究基于流行于欧美国家及港台地区多年的身心语言程式(NLP)理论,从身心、语言和程式三个核心维度出发,构建了信息检索用户的"需求认知、表达与交互模式"、"检索语言认知、表达与交互模式"和"检索目标制定中的心理取向模型".同时结合MP中的锚理论,分析信息检索用户的"心锚"和相关的"空间锚",总结出相应的信息检索规律,构建出检索中锚交互模型、集合关系、双因子矩阵模型和表达式,并对相关的假设模型进行数据检验和优化. 相似文献
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以基于图书馆领域本体的专业性视角解读当前图书馆文献资源检索技术,有针对性地对当前检索技术如全文检索、数据检索等做补丁处理,并提出了基于本体的图书馆文献资源检索模型的构建,基于该模型的特点阐述了信息检索的查全率和查准率是如何实现的。 相似文献
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因特网信息导航系统的建立与资源共享 总被引：1，自引：0，他引：1
网上信息的无序性、分散性、多样性给用户检索信息带来了不便，如何提高检索效率，为用户提供简洁方便的信息检索指引，即建立网络信息导航是趋势所在。本文介绍了网络导航系统组成及应解决的技术问题。包括搜索引擎、中文信息自动标引分类和检索及导航相关服务和全国导航分布体系的建立等。通过信息导航系统实现全国甚至全球的文献信息资源共享。 相似文献
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对数字图书馆统一检索平台的核心问题进行了介绍,在对XML和WebService介绍基础之上提出了基于XML与WebService相结合的数字图书馆同意检索的总体模型,并对如何利用该模型建立统一检索平台进行实证研究。 相似文献
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智能检索模型研究 总被引：2，自引：0，他引：2
孔敬 《现代图书情报技术》2005,21(3):37-42
提出了一个智能检索形式框架模型，论述了实例化该模型的建模技术、知识表示和检索算法，对30个智能信息检索系统进行了模型框架、知识表示和检索算法的统计分析，总结了三种类型的智能检索模型实例化方案。 相似文献
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个性化信息检索中的相关反馈技术研究 总被引：3，自引：0，他引：3
简要介绍了相关反馈的研究现状及基本思想,在深入分析相关反馈的实现策略和在不同系统中设计的差别后,提出了相关反馈技术和个性化信息检索结合的模型,最后讨论引入数据融合的思想来进一步改善反馈效果。 相似文献
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In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve
this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments.
Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it
is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach
is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study,
we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations
and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form
query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation
shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on
TREC retrieval. 相似文献
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We propose a method for performing evaluation of relevance feedback based on simulating real users. The user simulation applies
a model defining the user’s relevance threshold to accept individual documents as feedback in a graded relevance environment;
user’s patience to browse the initial list of retrieved documents; and his/her effort in providing the feedback. We evaluate
the result by using cumulated gain-based evaluation together with freezing all documents seen by the user in order to simulate
the point of view of a user who is browsing the documents during the retrieval process. We demonstrate the method by performing
a simulation in the laboratory setting and present the “branching” curve sets characteristic for the presented evaluation
method. Both the average and topic-by-topic results indicate that if the freezing approach is adopted, giving feedback of
mixed quality makes sense for various usage scenarios even though the modeled users prefer finding especially the most relevant
documents. 相似文献
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基于伪相关反馈的跨语言查询扩展 总被引：3，自引：2，他引：1
相关反馈是一种重要的查询重构技术,本文分析了两类相关反馈技术,一是按用户是否参与可分为伪相关反馈和交互式相关反馈,二是按作用于查询的方式可分为查询扩展与检索词重新加权.在此基础上,本文重点探讨了将相关反馈技术应用于跨语言信息检索,提出了翻译前查询扩展、翻译后查询扩展、翻译前与翻译后相结合的查询扩展三种方法.最后,本文通过伪相关反馈实验对这三种方法进行了比较,实验结果显示,三种跨语言查询扩展方法都能够有效地提高检索结果的精度,其中翻译后查询扩展方法相对更优越.此外,查询式的长度对不同跨语言查询扩展方法产生着不同程度的影响. 相似文献
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Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information. 相似文献
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Relevance feedback is an effective technique for improving search accuracy in interactive information retrieval. In this paper, we study an interesting optimization problem in interactive feedback that aims at optimizing the tradeoff between presenting search results with the highest immediate utility to a user (but not necessarily most useful for collecting feedback information) and presenting search results with the best potential for collecting useful feedback information (but not necessarily the most useful documents from a user’s perspective). Optimizing such an exploration–exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback. We formally frame this tradeoff as a problem of optimizing the diversification of search results since relevance judgments on more diversified results have been shown to be more useful for relevance feedback. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experiment results on three representative retrieval test collections show that the proposed learning approach can effectively optimize the exploration–exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation without exploration. 相似文献
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基于内容的图像检索反馈技术概述 总被引：2，自引：0，他引：2
在对现有的相关反馈算法进行总结的基础上，介绍一些有所改进的反馈算法，同时展示各种算法的基本原理和检索效果。通过对比各种算法的查全率与查准率，展示各种算法的优缺点、应用范围;介绍现有的几个使用相关反馈技术的基于内容的图像检索系统，并针对相关反馈领域的研究预测其有挑战性的发展方向。 相似文献
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相关反馈是近年来信息检索领域的研究热点，是自动查询扩展中的一种重要形式，相关反馈主要包括检索词加权和检索词选择。本文介绍了在相关反馈技术中经典的检索词排序算法，对它们带来的性能改进做了比较，并提出了相关反馈的实际应用中需要解决的一些问题。 相似文献