Temporal summaries of new topics
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to topic detection and tracking
Topic detection and tracking
The Journal of Machine Learning Research
Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding and linking incidents in news
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Mining common topics from multiple asynchronous text streams
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting topic evolution in scientific literature: how can citations help?
Proceedings of the 18th ACM conference on Information and knowledge management
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Connecting the dots between news articles
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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The Web is a great resource and archive of news articles for the world. We present a framework, based on probabilistic topic modeling, for uncovering the meaningful structure and trends of important topics and issues hidden within the news archives on the Web. Central in the framework is a topic chain, a temporal organization of similar topics. We experimented with various topic similarity metrics and present our insights on how best to construct topic chains. We discuss how to interpret the topic chains to understand the news corpus by looking at long-term topics, temporary issues, and shifts of focus in the topic chains. We applied our framework to nine months of Korean Web news corpus and present our findings.