Optimization in CLARIT TREC8 Adaptive Filtering

[摘要] In this paper, we describe the system andmethods used for the CLARITECH entries in the TREC8 Filtering Track.Our focus of participation was on the adaptive filtering task, as this comes closest to actual applications.In TREC7, we proposed, evaluated, and proved effective two algorithms for threshold setting and updating—the delivery ratio mechanism, which is used to obtain a profile threshold when no feedback has been received, and betagamma regulation, which is used for threshold updating.This year, we explored two ways of improving filtering performance given these our threshold setting algorithms as a basis by (1) allowing profilespecific anytime updating and (2) optimizing the other filtering system components, in particular, the retrieval/scoring mechanism and the profile vector learning. Our results show that profilespecific frequent updating indeed improves filtering performance. In addition, they suggest that optimizing the scoring function and the term vector learning component independently leads to even further improvement, providing another indication of the effectiveness and robustness of our threshold updating

[学科分类] 社会科学、人文和艺术（综合）