Dynamic Composition of Information Retrieval Techniques

信息检索技术的动态组合

• 批准号: 9907331
• 负责人: Shlomo Zilberstein
• 金额: $ 43.72万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9907331&HistoricalAwards=false
• 关键词: Dynamic; Composition; Information; Retrieval; Techniques

This is a collaborative project between the Information Retrieval Center and the Resource-Bounded Reasoning Lab at UMass. The project is aimed at developing a new approach to meta-level control of search and applying it to improve the flexibility, adaptability, quality of service, and robustness of information retrieval search engines. Currently, such systems are built by integrating a fixed set of modules and techniques that perform such tasks as query formation, query optimization, query evaluation, precision improvement, and recall improvement. The new approach consists of context-dependent mechanisms for optimal selection of information retrieval techniques based on a probabilistic description of their performance. The approach addresses effectively the high level of uncertainty regarding the duration of complex retrieval techniques and the quality of the result they produce. The current static approach to integration of information retrieval modules continues to produce performance gains of about 10% each year, but the systems are extremely specialized for each task, and it is not clear how well results will generalize to new types of retrieval. This project provides significant advantages because it allows a system to configure itself dynamically to the specific task at hand, to the person using the system, and to limited computational resources. This study will result in systems that are far more flexible in handling a large set of retrieval tasks with possible applications to a range of other problems such as dynamic selection of tasks for autonomous robots to optimize the quality of service. http://anytime.cs.umass.edu/shlomo/research/DCIR.html

这是麻省大学信息检索中心和资源约束推理实验室之间的一个合作项目。该项目的目的是开发一种新的方法，以元级控制的搜索和应用它来提高信息检索搜索引擎的灵活性，适应性，服务质量和鲁棒性。目前，这样的系统是通过集成一组固定的模块和技术来构建的，这些模块和技术执行诸如查询形成、查询优化、查询评估、精度改进和召回改进之类的任务。新的方法包括上下文相关的机制，最佳选择的信息检索技术的基础上概率描述其性能。该方法有效地解决了复杂的检索技术的持续时间和它们产生的结果的质量方面的高度不确定性。目前信息检索模块集成的静态方法每年继续产生约10%的性能增益，但系统对每项任务都非常专业，目前还不清楚结果将如何推广到新的检索类型。这个项目提供了显着的优势，因为它允许系统动态配置自己手头的特定任务，使用系统的人，有限的计算资源。这项研究将导致系统在处理大量检索任务时更加灵活，可能应用于一系列其他问题，例如自动机器人的任务动态选择，以优化服务质量。http://anytime.cs.umass.edu/shlomo/research/DCIR.html