Enhancement of Expert Retrieval Assistance Models and Techniques

专家检索辅助模型和技术的增强

• 批准号: 9014863
• 负责人: Richard Marcus
• 金额: $ 17.2万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-09-01 至 1993-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9014863&HistoricalAwards=false
• 关键词: Enhancement; Expert; Retrieval; Assistance; Models

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.

本项目将(1)完成现有设计的实验专家检索辅助系统在工作站环境下的实现；(2)通过实验和分析来检验系统的有效性；(3)根据分析结果，设计更精细、更全面的搜索模型和辅助技术。所使用的技术包含如下功能：(1)基于若干匹配标准谱估计绝对和相对搜索相关性；(2)利用当前基于布尔的系统中的现有工具，根据相关性对文档进行自动排序；(3)基于用户相关性反馈的搜索策略优化。这些新技术将与当前系统中的一系列辅助助手相结合，包括：(1)在不同数据库中自动执行搜索策略，包括从共同命令语言进行翻译；(2)基于用户自然语言短语的自动搜索策略；(3)基于形式搜索特征和经验搜索结果的召回估计。这些被称为“智能布尔”的模型和技术将推动信息检索从直觉艺术向理性的量化决策过程发展。