TOPIC TRACKING WITH IMPROVED REPRESENTATION MODEL AND JOINT TRACKING METHOD

Abstract

Topic tracking is to monitor a stream of stories to find additional stories on a topic identified by several samples. However, the predefined information about a tracked topic does not provide enough information to deal with the new information occurred in the tracking procedure. To overcome this problem, we proposed a joint tracking method using both the topic-specific information from the predefined information and the non-topic-specific information from the data on other topics. Besides, to overcome the limitation of the representation model and the topic drift problem, we have also used two other improvements: a topic-based weighting method is used to measure the features of both tracked topics and single testing stories; a dynamic topic model is extended with the information brought by the incoming related stories and the noise is filtered out with the information in the incoming unrelated stories. The implemented tracking systems are evaluated on the Chinese subset of TDT4 corpus by the TDT2003 evaluation method. The experimental results indicate that the above methods all improve the tracking performance. More importantly, these techniques are complementary to one another and not mutually exclusive.