正确理解和使用“中国地区表”的专类复分表——以“市”和“市辖区”为例

通过对国家图书馆、上海图书馆、CRLNet、CALIS有关"市"和"市辖区"文献的分类标引情况的调查,得到数据显示,省会(首府)城市、地级市和中央直辖市所属市辖区文献的分类标引比较准确一致;而县级市和其他市辖区文献的分类标引,则出现了多种结果并存的混乱现象。针对此,本文就作者个人的理解,对县级市和其他市辖区的涵义和定位,进行了合理解释。并在此基础上,提出了具体改进举措,以确保分类标引时的准确和一致。     

由中国专利探讨TFT-LCD专利表现及主要公司技术布局

本研究参考日本的FI／F-Term专利分类方式，将TFT-LCD制程相关技术分成8项技术主题，并从整体产业、国家（地区）、主要公司（专利案件总数排名前15名的专利权人）以及技术主题等观点出发，观察中国TFT-LCD制程相关技术的专利表现，并根据此8项技术主题探讨15家主要公司的技术布局。     

基于概率神经网络的文本自动分类研究

本文提出了将一种径向基网络的重要变形———概率神经网络应用于文本自动分类的研究 ,与常用的K 最邻近法相比 ,具有一定的优势 ,特别是在训练集中各类的训练样本数很不平衡时 ;与BP等其他神经网络相比 ,其特点是需调节的参数少 ,不需确定隐层数、隐层中的神经元数量等网络结构 ,比较容易使用。此外 ,从研究中的不同特征选择的评价函数来看 ,它们对分类有一定的影响 ,应用X2 统计进行特征选择的分类正确率最高 ,其次是文本证据权 ,而期望交叉熵的效果最差 ,说明特征选择在文本自动分类中也是非常重要的。     

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。     

基于支持向量机的文本自动分类试验研究

提出了将支持向量机应用于文本自动分类的研究，与常用的K-最邻近法相比，无论是对训练数据集还是测试数据集均具有一定的优势，而且不同特征选择方法对支持向量机的影响要比K-最邻近法小。此外，从研究中的不同特征选择的评价函数来看，它们对分类有一定的影响，应用X2统计进行特征选择的分类正确率最高，其次是文本证据权，而期望交叉熵的效果最差，说明特征选择在文本自动分类中也是相当重要的。     

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。     

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.