试论引文在自动标引中的应用

评介了60年代以来比较著名的几种引文标引理论,并在此基础上提出了“无标引的引文检索”方法。作者利用该方法的基本原理进行了局部试验,结果显示良好。认为如果将引文标引检索与主题标引检索结合起来,可更好地提高情报检索效率。     

浅析维普数据库的特色与利用

本文分析了维普数据库在标引、检索与利用上的特点，并提出了相应对策。     

基于本体的文本信息检索研究

本文对如何构建基于本体的文本信息检索系统进行了探讨．并认为，利用反映概念之间关系的领域本体指导主题标引，利用反映实体之间关系的领域本体指导实体关系标引，并以本体的形式表示文档替代物和查询表达式，可以进一步提高文本信息检索系统的性能。     

Topic discovery based on text mining techniques

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.     

‘Do you want me to translate this in English or in a better Mandinka language?’: Unequal literacy regimes and grassroots spelling practices in peri-urban Gambia

This paper presents a comparative ethnographic analysis of two versions of a grassroots text in Mandinka language, one written by a non-formally educated man, the other a respelling by a formally educated urbanite. The analysis points at a crucial difference in spelling practices and inequality in literacy regimes, i.e., between established orthographic English literacy and the more creative, heterographic and emergent local language literacy. It is shown how English orthography affects local ideologies of literacy as well as Mandinka spelling practices itself, in greater or lesser extent, depending on the educational history of the speller.     

一种改进的K-means算法最佳聚类数确定方法

对BWP方法进行研究,从嵌入式NSTL个性化推荐的文本聚类需求入手,分析BWP方法的不足,提出一种改进的K-means算法最佳聚类数确定方法。对单一样本类的类内距离计算方法进行优化,扩展BWP方法适用的聚类数范围,使原有局部最优的聚类数优化为全局最优。实验结果可以验证该方法具有良好性能。     

网络舆情信息提取与预处理研究

本研究针对舆情信息源特征就舆情信息增量采集、提取和存储、文本信息预处理提出了基于Web—Harvest的定点信息采集以及基于输入法平台的新词收集策略,构建了一个互联网用语扩展词库,实现了信息预处理关键模块。     

中文图书标引常见错误剖析

文章对中文图书标引错误进行了剖析,显示出当前的拷贝标引模式给标引质量带来的“杀伤力”,指出标引专业培训的缺失是标引质量问题频现的根源.     

A relational vector space model using an advanced weighting scheme for image retrieval

In this paper, we lay out a relational approach for indexing and retrieving photographs from a collection. The increase of digital image acquisition devices, combined with the growth of the World Wide Web, requires the development of information retrieval (IR) models and systems that provide fast access to images searched by users in databases. The aim of our work is to develop an IR model suited to images, integrating rich semantics for representing this visual data and user queries, which can also be applied to large corpora.     

Automatic categorization of questions for user-interactive question answering

Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions.