Dynamic Composition of Information Retrieval Techniques

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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