CLARIT Experiments in Batch Filtering:

Introduction The Clairvoyance team participated in the Filtering Track, submitting two runs in the Batch Filtering category. While we have been exploring the question of both topic modeling and ensemble filter construction (as in our previous TREC filtering experiments [5]), we had one distinct objective this year, to explore the viability of monolithic filters in classification-like tasks. This is appropriate to our work, in part, because monolithic filters are a crucial starting point for ensemble filtering, and it is possible for them to contribute substantially in the ensemble approach. Our primary goal in experiments this year, thus, was to explore two issues in monolithic filter construction: (1) term count selection and (2) filter threshold optimization. In fact, our pre-TREC experiments were conducted in a brief period and we were unable to complete all the tests we had planned. Our official submissions reflect essentially our first, baseline results. They are overall poor i