Dynamic Composition of Information Retrieval Techniques

这是马萨诸塞州信息检索中心和资源受限推理实验室的合作项目。该项目旨在开发一种新的元级搜索控制方法，并将其应用于提高信息检索搜索引擎的灵活性、适应性、服务质量和健壮性。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、精度改进和召回率改进等任务。新的方法包括基于对信息检索技术性能的概率描述的上下文相关机制，用于最佳选择信息检索技术。该方法有效地解决了关于复杂检索技术的持续时间及其产生的结果的质量的高度不确定性。目前整合信息检索模块的静态方法继续每年产生约10%的性能收益，但这些系统对每项任务都非常专门，目前尚不清楚结果将在多大程度上推广到新类型的检索。这个项目提供了显著的优势，因为它允许系统根据手头的特定任务、使用系统的人和有限的计算资源动态地配置自己。这项研究将导致系统在处理大量检索任务时更加灵活，并可能应用于一系列其他问题，例如为自主机器人动态选择任务以优化服务质量。Http://anytime.cs.umass.edu/shlomo/research/DCIR.html

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