基于潜语义标引的自然语言检索

在信息检索中, 向量空间模型是最有效的数学工具之一。由于自然语言检索的特殊性, 以及传统信息检索模型受到同义词、多义词的影响, 检索的查准率不高。为了提高自然语言检索的查准率, 我们对基于概念的信息检索模型——
潜语义标引(LS I) 模型进行了探讨, 并分析了基于LS I 的两个实例。     

基于多层次概念语义网络结构的中文医学信息语义标引体系和语义检索模型研究

为了提高中文医学信息检索效率,本文应用语义学研究成果,深入剖析统一医学语言系统(UMLS),从理论上对多层次概念语义网络结构进行了探讨,以此设计了适用于中文医学信息特点的三层概念语义网络结构,并分别确定了各个概念语义网络层次的语义类型和语义关系,进一步完善了医学信息语义网络.以信息检索的认知理论为依据,建立了基于三层概念语义网络结构的中文医学信息语义标引体系和语义检索模型.对扩展检索和语义检索进行统计学Kappa检验,认为两种检索方法的一致性非常显著(p<0.01);与扩展检索中的任何一种方法相比,语义检索方法具有更高的检索效率.     

LSI潜在语义标引方法在情报检索中的应用

介绍了一种基于词相依性的语义结构, 被称为“潜在语义标引”的文献自动标引和检索技术。采用词频统计和奇值分解技术来捕捉文献的语义结构, 得到标引词、提问和文献的向量表示, 检索系统可以预测文献与提问之间的相关度, 达到检索的目的。     

基于语义的馆藏信息检索模型研究

研究了语义网的概念及其层次模型，提出一种新型的基于语义的图书馆馆藏信息检索模型，给出了系统的架构体系，分析了系统实现的关键技术。该系统以语义网及本体为基础，将传统的基于关键词的检索提高到基于语义检索。     

基于共现分析的语义信息检索研究

提高信息检索系统的用户体验度可以从查询优化的算法和增强可视化展示的研究等方面入手。本文利用文献调研、共现分析和构建本体等方法,设计基于共现分析的语义信息检索和流程,并利用武汉大学图书馆的书目检索系统中"世界考古"类目的数据进行实验分析。经过文献调研发现,目前语义信息检索主要集中在基于本体的查询技术、语义标注问题和语义关系检索等方面的研究,语义信息检索目前只能做到表层相关的检索。构建的基于共现分析的语义信息检索模型包括规范器、分析器和本体构建器三个部分,其中分析器是本模型的核心。经过实验分析发现共现分析可以应用于语义信息检索,并比较得出基于共现分析的语义检索比传统检索更具人性化、引导性。     

基于知识标引的档案信息集成与检索系统研究

基于词首最长匹配的词典分词和段句分割符表的切分标记分词，构建用于档案信息集成与检索的知识标引环境，然后运用ASPNET分布式技术，研究和开发一个基于知识标引的异构档案数据整合与信息检索系统。     

统一的中国医学语言系统研究进展

“统一的中国医学语言系统”旨在建立一个计算机化的可持续发展的生物医学检索语言集成系统。该系统由3个部分组成：中国医学用语数据库，中国医学语语义网络系统，中国医学用语与标引检索语言对应转换系统。该系统的研制在医学文献计算机辅助标引方面发挥着积极作用，在一体化信息检索系统的开发研制方面具有重要作用。     

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。     

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。     

一种基于领域本体的语义检索系统的设计与实现

基于领域本体的语义检索被认为是解决目前信息检索领域中所面临的困难的途径之一。从语义推理、查询分析等几个关键方面对基于领域本体的语义检索进行研究,在此基础上设计并实现了一个基于领域本体的语义检索实验系统,通过测试表明该系统能够较为有效的实现语义检索,提高检索精度。     

Knowledge-Enhanced Latent Semantic Indexing

Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document matrix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. However, for many knowledge domains there are pre-existing semantic structures that could be used to organize and categorize information. The goals of this study are (i) to demonstrate how such semantic structures can be automatically incorporated into the LSI vector space model, and (ii) to measure the effect of these structures on query matching performance. The new approach, referred to as Knowledge-Enhanced LSI, is applied to documents in the OHSUMED medical abstracts collection using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall data (11-point average precision values) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain (as much as 35%) over the original LSI for modest constraints on precision. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matches.     

隐含语义检索及其应用

隐含语义检索(Latent Semantic Indexing, LSI) 是一种基于概念的文献检索方式。它区别于传统的基于用户查询条件与文档的单词匹配的文献检索方法, 根据文档与查询条件在语义上的关联而向用户提交查询结果。本文介绍了隐含语义检索在文献检索中的一种实现方法, 为文献检索提供了一种新的途径。     

我国图书馆学本土化研究述评

当前,我国的图书馆学本土化研究在概念、特征、目的、基本内容和研究者的主体意识等方面都取得了一定的研究成果,并辩证地处理好了本土化与国际化、理论性与实践性以及命题的真与伪等关系。但是,目前仍存在着理论与实践脱节、研究尚未形成一定的规模、研究文本与诠释之间存在距离、立足国情与创新不协调等问题。     

基于潜在语义索引的特征选择与权重改进若干关键问题的研究与实现

分析潜在语义索引的基本原理及其特点。针对LSI的3个因素，从特征词的选取，维数约简，特征词权重3个方面进行约定和改进。并以计算机类的科技文献作为测试文档，对改进的权重算法和改进前后LSI系统的检索结果进行分析。结果显示，特征词的选择结果及检索效果都得到较大的提高，性能稳定。     

Improved Query Matching Using kd-Trees: A Latent Semantic Indexing Enhancement

Efficient information searching and retrieval methods are needed to navigate the ever increasing volumes of digital information. Traditional lexical information retrieval methods can be inefficient and often return inaccurate results. To overcome problems such as polysemy and synonymy, concept-based retrieval methods have been developed. One such method is Latent Semantic Indexing (LSI), a vector-space model, which uses the singular value decomposition (SVD) of a term-by-document matrix to represent terms and documents in k-dimensional space. As with other vector-space models, LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query matching phase of LSI, a user's query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query matching method requires that the similarity measure be computed between the query and every term and document in the vector space. In this paper, the kd-tree searching algorithm is used within a recent LSI implementation to reduce the time and computational complexity of query matching. The kd-tree data structure stores the term and document vectors in such a way that only those terms and documents that are most likely to qualify as nearest neighbors to the query will be examined and retrieved.     

基于LSI理论的文本自动聚类研究

结合潜性语义索引（latent semantic index,LSI）理论和K-means聚类法,提出一种改进的文本自动聚类方法,即首先利用N-gram统计法抽取文档关键词,并应用潜性语义索引LSI对构建文档的向量空间模型进行降维,然后采用K-means算法进行文本聚类。实验表明,该算法进行文本聚类的准确度最高可达84.7%。     

Learning to rank with (a lot of) word features

In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.     

Listening and Experiences of Interpersonal Transcendence

Interpersonal transcendence is a phenomenon marked by total immersion in an interaction, a deep sense of understanding, feelings of mutuality, new insight, and playfulness. Such experiences can be exhilarating, memorable, and rare. This study examines relationships between various listening dispositions and people’s propensity to experience interpersonal transcendence. Participants (N = 300) completed the Interpersonal Transcendence Scale (ITS) along with the Listening Styles Inventory (LSI), the Active Empathic Listening scale (AEL), and the Listening Styles Profile–Revised (LSP-R). Results indicated substantial relationships between ITS responses and the LSI and AEL. The LSP-R dimensions of Relational Listening and Analytical Listening were also related to ITS responses. Other results indicate that propensity to experience interpersonal transcendence is associated with minority group status and age.     

二十一世纪前十年数字图书馆技术研究和系统建设的9个思潮

文章分析了二十一世纪前十年数字图书馆技术研究和系统建设的历程,总结了这十年来在数字图书馆技术研究和系统建设中的9个思潮：数字化和数字资源研究与建设思潮、门户研究与建设思潮、集成和互操作研究与建设思潮、“下一代数字图书馆”研究与建设思潮、数字仓储研究与建设思潮、数字保存研究与建设思潮、本体和语义网研究与建设思潮、Web2．0思潮、开放存取与开源思潮。