新时期我国文献传递服务的发展现状及路向分析

我国文献传递服务从上世纪20年代开始发展到现在,已呈现出欣欣向荣的发展态势。但在新时期逐步显现出发展不平衡、资源重复建设、缺少协调和评价、技术难突破等问题。为突破这些制约我国文献传递有效前行的瓶颈,新时期我国文献传递服务呈现出下列发展态势:直接面向用户的非中介式传递方式是发展趋势;地区—全国文献传递系统的建立是最佳选择;开辟特种文献及特色文献传递渠道是努力方向;产业化文献传递道路是必然归宿。     

利用文档加密技术防止企业知识资产的外泄

企业知识资产不仅需要创造,同样也需要保护。本文分析了常用的文档、操作系统、口令、物理隔离等加密手段的局限,提出了基于企业利益的文档安全管理目标,即实时透明加密、多种介质兼容、集中远程控制、操作程序限制、身份权限验证、动态授权管理六个方面;探讨了文档安全管理系统的工作原理和系统组成及其在企业中的应用策略,即采用数据库管理文档的方式、RBAC与DAC结合的动态授权机制以及自动透明加密技术策略等等。     

网络环境下文献传递模式的研究与构想

文献传递解决了用户对于一次文献的需求。当前在国内普遍存在四种文献传递的模式。随着数字图书馆解决方案的成熟,通过各情报机构及商业机构的协调,将最终实现方便快捷的传递模式。     

论网络环境下图书馆信息资源建设

文章分析了当前图书馆资源建设外部环境的特点,并对信息社会馆藏资源建设的新模式以及今后资源共建共享等问题进行了探讨。参考文献4。     

海阔凭鱼跃天高任鸟飞--论网络环境下文献检索课教师的出路

文章站在现代信息技术发展的角度,提出网络环境下文献检索课教师所肩负的重任.论述文检课教师面临网络信息技术的挑战,如何抓住机遇,加强自身综合素质的培养,积极开展教学研究,开辟文献检索领域的新天地.     

试论图书馆的非中介性文献传递服务

非中介性文献传递服务是国外图书馆普遍采用的一种文献传递方式,它消除了读者和文献供应商之间馆员介入的中转环节,从而带来时间和成本上的节约,极大地提高了服务效率。文章以国外一些大学图书馆开展该项服务的有关资料来分析非中介性文献传递服务的利弊,讨论该服务开展的必要性和可行性。     

Web document summarization by exploiting social context with matrix co-factorization

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.     

从信息素质教育看文检课教学的改革与创新

本文简述信息素质和信息素质教育的内涵与发展,介绍文检课教学的现状,指出文检课教学的改革与创新势在必行,提出了文检课进行教改的思路,即构建完善的多层次、多角度教学体系,整合教学内容,调动学生的信息需求,加强信息意识培养和信息道德教育,采用多样化的教学模式,加强师资队伍建设,钻研教学艺术等.     

An analysis on document length retrieval trends in language modeling smoothing

Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance of the Jelinek–Mercer method is improved by including a length-based prior.

基于主题的SE-TextRank 情感摘要方法

文本情感摘要技术的目的是以简洁的形式准确表达文章的核心情感内容。为解决不同的文档结构及内容特征等问题对摘要结果的影响,提出了一种基于主题的SE-TextRank 情感摘要方法。通过LDA 模型自动获取收敛后的文本主题,利用余弦距离算法进行主题句子分组,使用传统多特征融合以及SE-TextRank 情感摘要算法对组内中心句抽取,最终获取目的摘要。实验表明,采用此方法能够更为高效的获取新闻文本摘要结果。