Enhancement of Expert Retrieval Assistance Models and Techniques

本项目将（1）完成 实验性专家检索辅助的现有设计 系统在工作站环境下的运行;（2）测试系统的有效性 通过实验和分析，对系统的性能进行了研究;（3） 根据分析的结果，设计更精细的 以及全面的搜索模型和辅助技术。 的 所使用的技术结合了这样的功能：（1）估计 基于多个光谱的绝对和相对搜索相关性 匹配标准;（2）自动排序的文件， 相关性使用现有的工具，从当前的基于布尔 系统;（3）基于用户的搜索策略优化 相关反馈 这些新技术将与 现有系统中的一批援助人员，包括 (1)自动执行搜索策略在不同的 数据库，包括从公共命令语言的翻译; (2)根据用户的自然特征自动推导出的搜索策略 语言短语;和（3）召回估计的基础上，正式 搜索特征和经验搜索结果。 这些模型和 技术，被称为“智能布尔”，将推进信息 从直观的艺术到理性的量化检索 决策过程

This project will (1) complete the implementation of the existing design for an experimental expert retrieval assistance system in the workstation environment; (2) test the effectiveness of the system through experimentation and analysis; and (3) depending on the results of the analysis, devise more refined and comprehensive search models and assistance techniques. The techniques used incorporate such functionality as (1) estimating absolute and relative search relevance based on several spectra of matching criteria; (2) automatic ranking of documents by relevance using existing tools from current Boolean-based systems; and (3) optimization of search strategies based on user relevance feedback. These new techniques will be integrated with a collection of assistance aides in the current system including (1) automatic execution of search strategies in different databases including translation from a common command language; (2) search strategies derived automatically from user's natural language phrases; and (3) recall estimation based on both formal search features and empirical search results. These models and techniques, termed "smart Boolean", will advance information retrieval from an intuitive art toward a rational quantified decision-making process.