document: Topic-conditioned novelty detection Automated detection of the first document reporting each new event in temporally-sequenced streams of documents is an open challenge . In this paper we propose a new approach which addresses this problem in two stages : 1 ) using a supervised learning algorithm to classify the on-line document stream into pre-defined broad topic categories , and 2 ) performing topic-conditioned novelty detection for documents in each topic . We also focus on exploiting named-entities for event-level novelty detection and using feature-based heuristics derived from the topic histories . Evaluating these methods using a set of broadcast news stories , our results show substantial performance gains over the traditional one-level approach to the novelty detection problem .
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