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《Information processing & management》2022,59(5):103051
In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. In practice, a suitable workflow can provide guidelines for constructing a machine-mediated agent replacing of human agents. Therefore, we conduct a comparison analysis and summarize two challenges when directly applying the conversational agent workflow in web search to legal case retrieval: (1) It is complex for agents to express their understanding of users’ information need. (2) Selecting a candidate case from the SERPs is more difficult for agents, especially at the early stage of the search process. To tackle these challenges, we propose a suitable conversational agent workflow in legal case retrieval, which contains two additional key modules compared with that in web search: Query Generation and Buffer Mechanism. A controlled user experiment with three control groups, using the whole workflow or removing one of these two modules, is conducted. The results demonstrate that the proposed workflow can actually support conversational agents working more efficiently, and help users save search effort, leading to higher search success and satisfaction for legal case retrieval. We further construct a large-scale dataset and provide guidance on the machine-mediated conversational search system for legal case retrieval. 相似文献
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This paper studies how to learn accurate ranking functions from noisy training data for information retrieval. Most previous work on learning to rank assumes that the relevance labels in the training data are reliable. In reality, however, the labels usually contain noise due to the difficulties of relevance judgments and several other reasons. To tackle the problem, in this paper we propose a novel approach to learning to rank, based on a probabilistic graphical model. Considering that the observed label might be noisy, we introduce a new variable to indicate the true label of each instance. We then use a graphical model to capture the joint distribution of the true labels and observed labels given features of documents. The graphical model distinguishes the true labels from observed labels, and is specially designed for ranking in information retrieval. Therefore, it helps to learn a more accurate model from noisy training data. Experiments on a real dataset for web search show that the proposed approach can significantly outperform previous approaches. 相似文献
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Current Web-based search engines presume a category search for a specific group of users. This approach is appropriate for generalized information searches since it is based on statistically generated user profiles. However, in some applications, such as medicine and law, an individualized search for a specific user at a given point in time is desired. In addition, the use of specialized terminology in some fields necessitates guidance for the non-expert to be successful in locating the desired information. This paper presents a new decision support system enabled by the analytic hierarchy process and intelligent software agents that can be used by researchers and practitioners in technical fields to aid information retrieval and improve search results from a controlled vocabulary. An application from telemedicine is given to illustrate the potential improvements. 相似文献
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基于潜在语义分析的影响自然语言检索查准率指标因素的评述 总被引：1，自引：0，他引：1
潜在语义分析是自然语言使用于情报检索系统的理论基础,以此理论建构的空间向量模型是评判检索系统性能优良与否的知识工具。阐述了潜在语义标引(LSI)的基本内容、LSI下影响自然语言检索查准率的因素及向量空间模型检索软件的运行机制。此评述对网络化的情报检索技术的发展起到了一定的参考作用。 相似文献
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在前人相关研究的基础上,提出了一个搜索引擎网站用户忠诚度影响因素的研究模型,分析了搜索引擎网站用户忠诚度影响因素主要包括可用性、知名度、信任、满意度和信息检索模型评价等,此外,用户对搜索引擎的熟悉度对用户的信息获取行为也起到了重要的作用。 相似文献
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网络环境下的信息检索与数据挖掘技术 总被引：1，自引：0，他引：1
首先对网络环境下信息检索的现状进行分析。主要介绍网络信息检索的代表工具一搜索引擎的工作原理、缺陷夏发展方向,引出数据挖掘技术。并进一步对WEB数据挖掘技术作了概要的介绍,阐明WEB数据挖掘技术是网络信息检索智能化的重要发展方向之一。最后,提出一个结合数据挖掘技术的新的搜索引擎结构模型。 相似文献
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利用分类法和主题法改善搜索引擎的性能 总被引：6，自引：0，他引：6
本文对Internet上的检索工具搜索引擎的工作机理和性能进行了全方位的探讨，指出了常见搜索引擎信息检索缺点。同时还指出了Meta搜索引擎、智能搜索引擎和代理搜索引擎虽然提高了网络信息检索的质量，但由于分类体系不统一，类目划分标准模糊，因而仍然未能从根本上改变搜索引擎主要以关键词（自然语言）作为检索入口的现状，不能实现分类检索与主题检索的一体化。要实现搜索引擎信息检索的突破，笔者认为有必要运用情报检索语言的理论和方法来完善因特网搜索引擎的性能，实现分类、主题一体化的检索机制，克服分类检索语言单纯以学科聚类、主题语言单纯以事物聚类的局限性。 相似文献
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《Information processing & management》2019,56(4):1260-1279
This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations.We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters.A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions. 相似文献
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Fidel Cacheda Victor Carneiro Vassilis Plachouras Iadh Ounis 《Information processing & management》2007
The increasing number of documents that have to be indexed in different environments, particularly on the Web, and the lack of scalability of a single centralised index lead to the use of distributed information retrieval systems to effectively search for and locate the required information. In this study, we present several improvements over the two main bottlenecks in a distributed information retrieval system (the network and the brokers). We extend a simulation network model in order to represent a switched network. The new simulation model is validated by comparing the estimated response times with those obtained using a real system. We show that the use of a switched network reduces the saturation of the interconnection network, especially in a replicated system, and some improvements may be achieved using multicast messages and faster connections with the brokers. We also demonstrate that reducing the partial results sets will improve the response time of a distributed system by 53%, with a negligible probability of changing the system’s precision and recall values. Finally, we present a simple hierarchical distributed broker model that will reduce the response times for a distributed system by 55%. 相似文献
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介绍了Agent的概念和移动Agent技术,探讨了Agent技术在商品信息检索、商务谈判和电子支付三个方面的应用方式,结合应用实例验证了采用Agent技术实现智能化电子商务的可行性。 相似文献
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随着计算机技术、通信技术、网络技术的迅猛发展,数字图书馆已成为高校建设与发展的重要组成部分。而伴随着图书馆数字化建设程度的提高,计算机检索已成为主要检索方式。面对信息网络环境,高校文检课教学必须探索出一条适应数字环境和网络环境的新路子以实现高校人才培养的总体目标。 相似文献
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科技信息的网络动态监测和信息自动获取技术研究 总被引：1，自引：0，他引：1
文章综述了国内外基于Robot的Web信息检索(IR)、定题检索、智能信息检索及其代理以及相关的信息自动获取技术的有关理论、方法和技术,探讨了适合技术预测与评估的需要、用于科学领域专题信息采集的、网络科技信息智能获取的方法和策略。构建了网络科技信息动态监测和信息自动获取系统的总体框架,实现了一个基于Web的系统原型(简称BIT),并分析了该系统的特点。 相似文献
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Agent是人工智能和计算机软件领域中一种新兴的技术，其在远程教育中的应用具有很大的优越性。本文在分析传统的基于Web信息检索课远程教育不足的基础上，探讨了用Agent思想解决远程教育的问题，并设计了一个基于Agent的信息检索课教学系统模型。 相似文献
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[目的/意义]大数据时代智慧图书馆用户需要精确智能的检索工具，而移动视觉搜索技术能够满足用户以视觉资源数据为中心的检索需求。[方法/过程]文章在分析国内外基于深度学习的视觉资源识别技术的研究基础之上，构建了基于深度学习的智慧图书馆移动视觉搜索服务模式模型，并设计了模型的工作流程，最后对基于深度学习的智慧图书馆移动视觉搜索系统的发展趋势进行了展望。[结果/结论]将深度学习融入智慧图书馆移动视觉搜索系统中，可以整合多源异构视觉数据、贴合用户个性化偏好以及提高移动视觉搜索系统性能。 相似文献
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个性化网络信息智能检索系统设计 总被引：3，自引：0，他引：3
本文结合现有的智能技术，提出了一个个性化网络信息智能检索模型。该模型能够对用户行为进行学习，从而建立一个反应用户兴趣的用户模型。而针对网络分布式的特点建立的检索代理模型，则能够通过对大量用户数据的分析积累丰富的知识。通过用户模型与检索代理模型的交互过程，向用户提供满意的检索结果。 相似文献
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John E. Leide Andrew Large Jamshid Beheshti Martin Brooks Charles Cole 《Information processing & management》2003,39(6):923-940
In this article and two other articles which conceptualize a future stage of the research program (Leide, Cole, Large, & Beheshti, submitted for publication; Cole, Leide, Large, Beheshti, & Brooks, in preparation), we map-out a domain novice user’s encounter with an IR system from beginning to end so that appropriate classification-based visualization schemes can be inserted into the encounter process. This article describes the visualization of a navigation classification scheme only. The navigation classification scheme uses the metaphor of a ship and ship’s navigator traveling through charted (but unknown to the user) waters, guided by a series of lighthouses. The lighthouses contain mediation interfaces linking the user to the information store through agents created for each. The user’s agent is the cognitive model the user has of the information space, which the system encourages to evolve via interaction with the system’s agent. The system’s agent is an evolving classification scheme created by professional indexers to represent the structure of the information store. We propose a more systematic, multidimensional approach to creating evolving classification/indexing schemes, based on where the user is and what she is trying to do at that moment during the search session. 相似文献
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基于科技文献检索的信息发现与信息检索,探讨VIP、CNKI、万方数据知识服务平台系统功能在课题检索中的应用。在信息发现方面,综合运用各数据库系统功能寻找检索概念的同义词、课题相关的分类号和所属学科类别、抽象检索概念的相关词、课题相关的研究机构和研究者。在信息检索方面,根据各系统的功能特点不同,制定相应的课题检索策略——主题 相似文献