Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks

This paper introduces the special issue, and reviews the routing and filtering tasks as defined and evaluated at TREC. The tasks attempt to simulate a specific service situation: the system is assumed to process an incoming stream of documents against profiles of user interest, strictly in the time order in which they arrive, and immediately refer any matching document to the user. In the adaptive filtering version of the task, the user is assumed to provide a relevance judgement instantly. The rationale for the task definitions and the evaluation measures used is discussed.     

“信息可视化”专辑简介

张进博士现任美国威斯康星大学密尔沃基分校信息研究学院副教授,曾任武汉大学教授,情报科学系系副主任。张进博士的主要研究领域包括：情报检索可视化、情报检索算法、Internet搜索引擎、Internet源信息组织、数字图书馆、人机接口等课题项目。这次应本刊邀请特为国内广大读者组织专栏稿件。编辑部代表广大读者对张进博士为本刊所组的信息可视化专辑表示衷心的感谢!     

INEX历年项目及其发展

INEX是当今信息检索领域最重要的国际评测会议之一.文章通过对INEX 2004年至2010年检索评价项目数量、项目类型、项目任务、测试集的变化,以及对IST 2007年至2010年所关注项目,参与INEX评测的机构的分析,了解XML检索领域的发展方向与趋势,以促进我国科研团队在XML语言检索评价领域更加深入的探索和发展.     

图书馆专用公共网络浏览器介绍

首先对公共浏览器的概念进行了说明，然后分析了图书馆公共浏览器的使用现状以及存在的问题，最后介绍国外使用较多且技术较为成熟的两个专用公共浏览器（K-Meleon和PWB）的功能以及设置使用方法。     

论广义借号法

简介了《中图法》特殊编号方法使用研究现状,根据类号位数与类级具有非一一对应性,存在跳级或并级现象,均可归属借号法的本质特征,提出将其统称为广义借号法。并从各类型方法的定义、类级特征、类号使用特点、优缺点、类目使用数量和使用中应注意的问题等方面论述了广义借号法体系     

INEX 2005 Dagstuhl XML信息检索国际会议述评

INEX是致力于XML信息检索与评价研究的国际性论坛，每年都有来自全球各地的专家学者参加其组织的研究活动和学术会议。本文介绍了INEX 2005年年终会议的概况、会议关注的焦点及未来的发展等。     

A Bayesian Framework for XML Information Retrieval: Searching and Learning with the INEX Collection

Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents—which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents.