The following work describes our solutions to the detection and tracking problems defined by the Topic Detection and Tracking (TDT2) research initiative. We discuss the implementation and results of the approaches which were recently tested on the TDT2 evaluation corpus. Our solutions to these problems extend text-based ranked retrieval techniques previously used for document clustering and filtering tasks. We present the effects of different on-line hierarchic clusterings on the detection task. In addition we compare adaptive and static approaches for building linear text classifiers for the tracking task.