关于自然语言检索问题

自然语言检索当前仅在关键词检索的层次上已经实现，重点分析了全文检索所适应和不适应的检索要求及其评价，用分析说明了当前关于自然语言检索的主流观点的不切实际，指出自然语言检索的发展方向是与情报检索语言结合和融合。     

对《中图法》(4版)民族文献归类的探讨

以民族类文献及其分类工作为出发点，针对《中图法》(第四版)民族文献的类目设置，建议增设民族学科相关类目和类目注释。     

精确数据的模糊查询工具的设计与实现

针对人员信息管理的数据查询操作，在SQL语言中定义了模糊词隶属函数和语言算子隶属函数的函数及相关存储过程，以实现人员信息管理数据库的模糊查询。     

分类法在搜索引擎中的运用思考

本文从目前搜索引擎中使用分类法的现状入手，针对图书情报界提出的优化搜索引擎中分类体系的改造方案，提出了一些有用的建议。     

情报检索语言的发展方向问题--分类主题一体化新论

对国内外五种著名分类主题一体化词表作了剖析，从新的视角揭示了一体化词表的实质，并根据情报检索语言的发展现状，阐明了分类主题一体化的前景，从而看清情报检索语言的发展方向。     

国外网络分类法的发展及对我国网络分类法理论与实践的启示

论文在分析网络分类法与传统文献分类法差异的基础上，阐述了国外网络分类法的特点、问题、发展趋势以及对我国网络分类法理论与实践的几点启示。     

基于粗糙集加权的文本分类方法研究

文本自动分类是当前智能信息处理中一类重要的研究课题。本文分析了基于统计理论的文本分类的基本特点,提出采用可变精度粗糙集模型中的分类质量构造新的特征词权重计算公式。这种新的加权方法,相对于广泛使用的逆文本频率加权方法,大大改进了文本样本在整个空间中的分布,使得类内距离减少,类间距离增大,在理论上将提高样本的可分性。最后利用支持向量机和K近邻两种分类器,验证了这种新的加权方法对分类效果确实有所提高。     

简评三段式分类索刊号——与施振宏、王觊商稚兼谈刊次号设计

针对三段式分类索刊号的编号方法及在实际应用中存在的不足提出商讨意见，并对刊次号的设计提出看法。     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.