Term Weighting Schemes for Emerging Event Detection

Abstract

As an event-based task, Emerging Event Detection (EED) faces the problems of multiple events on the same subject and the evolution of events. Current term weighting schemes for EED exploiting Named Entity, temporal information and Topic Modeling all have their limited utility. In this paper, a new term weighting scheme, which models the sparse aspect, global weight and local weight of each story, is proposed. Then, an unsupervised algorithm based on the new scheme is applied to EED. We evaluate our approach on two datasets from TDT5, and compare it with TFIDF and existing two schemes exploiting Topic Modeling. Experiments on Retrospective and On-line EED show that our scheme yields better results.