用CNMARC代替KORMARC进行韩文文献编目的可行性分析

通过分析目前国内韩文文献编目的现状及问题,结合我校图书馆韩文编目的实践,基于ISBD(M)对CNMARC与KORMARC的著录项进行了比较,表明套用CNMARC进行韩文文献编目是完全可行的,以期为各图书馆在进行韩文编目时提供参考.     

用户自然和社会属性对网络搜索中语言使用行为的影响

以用户的自然、社会属性对用户在搜索中使用检索语言的影响为研究对象,旨在对影响用户行为的因素作探索性研究。综合网络调查问卷的分析结果和用户参与对比实验法,得出性别、年龄、学历和专业教育对用户语言使用影响的结论。对于此问题的研究,有助于认清搜索过程的影响因素,进而可以以此建模,改善搜索引擎的服务质量。     

基于ontology的语义检索模型架构

目前的web信息检索中，存在着检索效率低下的问题，很难满足普通用户基于自然语言的检索要求，也不具备语义推理能力，无法实现智能化的检索。而源于知识工程和人工智能领域的本体理论和技术，能够很好地处理自然语言理解问题和具有基于语义的推理机制，因此成为改进传统信息检索方式的良好工具。本文就运用本体理论，在对目前的各种研究进行了调查以后，区别了几个关键的容易混淆的概念，然后提出了一个基于ontology的、web环境下的语义检索系统的结构模型。     

论网络环境下情报检索语言的发展趋势

本文论述了网络环境下采用自然语言是必然趋势,然而自然语言并不会成为网络环境下的唯一检索语言,自然语言和人工语言相结合才是发展的方向.参考文献8.     

情报检索语言研究的新领域

情报检索语言的发展面临现代化、理论方法综合化和集成化的新条件,研究新的语言模型,提高检索语言的表达、描述等功能是当前的重要研究课题,同时要具体研究数据库检索语言、网络检索语言、本体论和语义网检索语言。在所有这些领域里,检索语言的机器理解功能日益重要;为适应机器处理的环境,对它的逻辑推理能力的要求越来越高,知识建模功能与系统表达能力受到越来越多的重视。     

西文手稿资料的编目实践与探索

论文以ISBD、AACR2相关规则为依据，结合对西文手稿资料范围、类型及特点分析，探讨西文手稿资料的著录规则与西文普通图书著录规则的差异，并对采用MARC21进行西文手稿资料编目方法及注意事项进行细致的分析与探讨。     

授权影印版西文图书的编目探讨

在对授权影印版西文图书的特征进行分析描述的基础上，结合西文文献编目原则及MARC21著录格式，探讨了该类出版物的文种归属问题及其编目方法，并提出了建议。     

医院小型图书馆工具书的收藏与利用策略

针对小型医学图书馆的特点，对工具书在图书馆工作中的作用以及工具书收藏进行了探讨，并且对工具书的利用策略进行分析。     

Two-stage statistical language models for text database selection

As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.     

Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments

Search facilitated with agglomerative hierarchical clustering methods was studied in a collection of Finnish newspaper articles (N = 53,893). To allow quick experiments, clustering was applied to a sample (N = 5,000) that was reduced with principal components analysis. The dendrograms were heuristically cut to find an optimal partition, whose clusters were compared with each of the 30 queries to retrieve the best-matching cluster. The four-level relevance assessment was collapsed into a binary one by (A) considering all the relevant and (B) only the highly relevant documents relevant, respectively. Single linkage (SL) was the worst method. It created many tiny clusters, and, consequently, searches enabled with it had high precision and low recall. The complete linkage (CL), average linkage (AL), and Ward's methods (WM) returned reasonably-sized clusters typically of 18–32 documents. Their recall (A: 27–52%, B: 50–82%) and precision (A: 83–90%, B: 18–21%) was higher than and comparable to those of the SL clusters, respectively. The AL and WM clustering had 1–8% better effectiveness than nearest neighbor searching (NN), and SL and CL were 1–9% less efficient that NN. However, the differences were statistically insignificant. When evaluated with the liberal assessment A, the results suggest that the AL and WM clustering offer better retrieval ability than NN. Assessment B renders the AL and WM clustering better than NN, when recall is considered more important than precision. The results imply that collections in the highly inflectional and agglutinative languages, such as Finnish, may be clustered as the collections in English, provided that documents are appropriately preprocessed.