Evaluating the effectiveness of content-oriented XML retrieval methods

Content-oriented XML retrieval approaches aim at a more focused retrieval strategy: Instead of retrieving whole documents, document components that are exhaustive to the information need while at the same time being as specific as possible should be retrieved. In this article, we show that the evaluation methods developed for standard retrieval must be modified in order to deal with the structure of XML documents. More precisely, the size and overlap of document components must be taken into account. For this purpose, we propose a new effectiveness metric based on the definition of a concept space defined upon the notions of exhaustiveness and specificity of a search result. We compare the results of this new metric by the results obtained with the official metric used in INEX, the evaluation initiative for content-oriented XML retrieval.

对"复合图书馆"提法的分析与质疑

近年来，有的研究者提出“复合图书馆”的概念。本文作者就这一概念的提法，在对其进行分析的基础上提出质疑，认为：这一提法在逻辑上是不通的、在内容上没有独立的见解、在效果上会造成误导。作者这种看法对否，以期引起讨论。     

Spelling correction in the PubMed search engine

It is known that users of internet search engines often enter queries with misspellings in one or more search terms. Several web search engines make suggestions for correcting misspelled words, but the methods used are proprietary and unpublished to our knowledge. Here we describe the methodology we have developed to perform spelling correction for the PubMed search engine. Our approach is based on the noisy channel model for spelling correction and makes use of statistics harvested from user logs to estimate the probabilities of different types of edits that lead to misspellings. The unique problems encountered in correcting search engine queries are discussed and our solutions are outlined.     

System Performance and Natural Language Expression of Information Needs

Consider information retrieval systems that respond to a query (a natural language statement of a topic, an information need) with an ordered list of 1000 documents from the document collection. From the responses to queries that all express the same topic, one can discern how the words associated with a topic result in particular system behavior. From what is discerned from different topics, one can hypothesize abstract topic factors that influence system performance. An example of such a factor is the specificity of the topic's primary key word. This paper shows that statements about the effect of abstract topic factors on system performance can be supported empirically. A combination of statistical methods is applied to system responses from NIST's Text REtrieval Conference. We analyze each topic using a measure of irrelevant-document exclusion computed for each response and a measure of dissimilarity between relevant-document return orders computed for each pair of responses. We formulate topic factors through graphical comparison of measurements for different topics. Finally, we propose for each topic a four-dimensional summarization that we use to select topic comparisons likely to depict topic factors clearly.     

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.     

基于平行语料的查询翻译词典改进方法研究

面对基于双语词典的跨语言检索查询翻译方法中固有的一对多等翻译模糊问题,已有研究成果存在对于非组合型复合词无法进行准确翻译、双语词典和其他翻译资源联合使用引入较大计算开销等弊端。为建立英汉双向跨语言检索实用性系统,在现有的一部包含若干科技词汇和短语的双语科技词典的基础上,着重研究如何引入平行语料来改进已有的双语词典问题。目标是生成一部基于句对齐平行语料的科技类双语概率词典,为跨语言检索查询翻译消歧提供实时性支持。     

一种基于关键词的大规模DeepWeb信息检索系统

针对传统web数据集成系统实用性、伸缩性和适应性差的问题,提出了一种新的web 数据集成系统体系结构,实现web规模的数据集成。系统支持用户提交关键词查询、提取用户查询模式、映射相关领域、选择web数据库、执行查询排序查询结果。介绍了组成系统的关键组件,及创建Deep Web索引、领域映射和用户模式匹配等处理大规模异构web数据的关键技术。     

Web整合中的资源描述技术

介绍Web整合中的关键技术--资源描述技术的内涵。在总结现有的基于STARTS协议、基于提问取样技术和调焦提问探测技术三种资源描述技术的基础上,分析每种技术的原理、算法、特点等,在此基础上对目前Web整合中的资源描述技术进行简要评价。     

一种基于查询上下文的个性化检索模型研究

个性化检索是信息检索领域研究的热点。要实现个性化检索必须收集用户兴趣。用户兴趣不能一概而论,针对不同的查询,用户的兴趣应该不同。选取与当前查询相关的检索历史构建查询上下文,通过查询上下文对检索结果进行重新排序。实验证明,个性化检索性能有所提高,提高的因素来自于最临近的几次检索历史,而更长的历史数据会使系统的运行效率下降,同时还会带来嗓音。
     

一种实用型跨语言检索查询翻译接口的设计与实现

面对日益膨胀的多语种信息资源,跨语言信息检索已成为实现全球知识存取和共享的关键技术手段。构建一个实用型的跨语言检索查询翻译接口,可方便地嵌入任意的信息检索平台,扩展现有信息检索平台的多语言信息处理能力。该查询翻译接口采用基于最长短语、查询分类和概率词典等多种翻译消歧策略,并从查询翻译的准确性和接口的运行效率两个角度对构建的查询翻译接口进行评测,实验结果验证所采用方法具有可行性。