This document describes a topic detection and tracking (TDT) system that uses semantic classes to represent documents. It splits documents into categories like names, locations, terms and temporals. Documents are represented as event vectors of these classes. The system compares event vectors class by class using metrics like cosine similarity. Experiments on a news corpus show the system achieves reasonable performance on tasks like topic tracking and first story detection, though performance degrades without vagueness factors. Semantic augmentation did not improve results as expected.