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Novelty detection based on sentence level patterns
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Using names and topics for new event detection
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Compare&contrast: using the web to discover comparable cases for news stories
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New event detection based on indexing-tree and named entity
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Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts
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Event identification in web social media through named entity recognition and topic modeling
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Context-based microblog browsing for mobile users
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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.