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。     

供需互动实现双赢——图书馆采编业务外包的成功案例介绍

介绍中山大学东校区图书馆和广东学苑文化发展有限公司进行采编业务外包合作的案例，包括该项目的前期准备工作、项目实施过程和实施结果；指出该案例的成功在于管理思想先进，服务意识、成本意识和质量意识强，管理机制灵活等因素；最后探讨该案例的推广意义。     

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。     

高校图书馆数据库的采购

本文总结了数据库采购的策略、方式、流程和存在的问题，并根据存在的问题提出了相应的对策。     

中文自然语言理解技术与智能检索

本文论述什么是中文自然语言理解技术，详细说明如何理解，智能搜索引擎如何应用中文自然语言理解技术进行检索。     

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。     

图书馆与图书市场互动--变革与发展路向研究

改革开放以来，以图书馆采编业务外包为内核的“服务”作为商品的有机组成部分进入图书馆市场，增添了这个传统市场与时俱进的特色。文章概述了当前图书馆市场的基本状况，指出其中存在的一些问题，并对如何培育好图书馆市场发表了看法和意见。     

图书馆图书采购模型的探讨与应用

通过定量分析方法,建立了资金在各图书类别中的分配模型,以及同类别下不同图书复本量的确定模型。模型有助于合理分配资金,达到图书采购预期效用。表1。图2。公式1。参考文献6。     

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.