Explorations Within Topic Tracking and Detection

Abstract

This chapter presents the system used by the Center for Intelligent Information Retrieval (CIIR) at the University of Massachusetts for its participation in four of the five TDT tasks: tracking, detection, first story detection, and story link detection. For each task, we discuss the parameter setting approach that we used and the results of our system on the test data.

For the task of link detection, we look more carefully at score normalization across different languages and media types. We find that we can improve results noticeably though not substantially by normalizing scores differently depending upon the source language. We also consider smoothing the vocabulary in stories using a “query expansion” technique from Information Retrieval to add additional words from the corpus to each story. This results in substantial improvements.

In addition, we use TDT evaluation approaches to show that the tracking performance that sites are achieving is what is expected from Information Retrieval technology. We further show that any first story detection system based on a tracking approach is unlikely to be sufficiently accurate for most purposes. Finally, we present an overview of an automatic timeline generation system that we developed using TDT data.

Russell Swan was the primary investigator and author for the automatic timeline construction work discussed in this chapter. He passed away unexpectedly after completing the work but before this chapter was published.