跨语言综合搜索引擎设计

分析了当前网络上信息分布的特点, 对目前相关的技术进行评价。在此基础上, 设计了一个解决网络搜索过程中语言障碍的系统——跨语言综合搜索引擎。     

目录及其在分布式主题网关中的应用

介绍了目录的基本概念，分析了LDAP、WHOIS 、CIP目录协议的主要特点，研究了国外主要的主题网关项目(ISAAC、ROADS、DESIRE、Imesh ToolKit)中目录协议的应用情况，指出目录协议在分布式索引、查询路由中起着的重要作用。     

视频数据库及其应用

视频数据库是多媒体数据库研究的继承和深入。本文阐述了视频数据库研究的主要问题，介绍了视频数据库系统的发展，并通过Hampapur提出的视频数据库的框架，旨在向读者展示视频数据库的优越性以及在各方面的应用。     

数字图书馆包装层生成技术研究

包装层是对应于特定信息源的一种特殊程序,是实现异构信息源集成的关键。数字图书馆的包装层可以作为数字图书馆的智能前端代理,负责把来自中介层的统一查询表示映射为针对具体数字图书馆的查询格式,并从得到的查询结果中提取出用户需要的信息,提交给中介层。数字图书馆包装层生成的关键技术主要有:查询映射机制,查询服务调用机制,结果提取与转换机制。图6。参考文献5。     

Two-stage statistical language models for text database selection

As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.     

重构式MIS系统构造模型的研究与实现

在深入剖析国内通用MIS系统开发和应用实际情况的基础上,提出一种基于重构的信息系统构造模型.该模型提供数据库重构模块、查询重构模块和界面解析模块,实现在系统中重新定义数据库对象、重新设计查询界面并保持应用程序和后台数据库的逻辑独立性.实践证明,基于重构式构造模型的MIS系统从根本上降低了维护成本,增强了系统的扩展性和应变能力.     

英语请求语用发展研究──以探询型请求子策略之选择为例

通过语篇补全测试,调查中国英语学习者对探询型请求子策略的选择。结果发现：学习者在请求主体策略选择上呈现与英语母语者相同的序列;学习者在请求子策略选择上存在过多或不当使用某些情态词,某些话句类型使用单一的情况;角色指向性的选择上,以听话者为指向的比率较高。这些特点表明文化间既具有相通性也存在文化相异,需要在教学中积极开展明示语用教学,重视学生语言能力的培养。     

Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm

The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423–431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.     

Optimization of some factors affecting the performance of query expansion

This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed.     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.