Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Classifying news stories using memory based reasoning
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Threading electronic mail: a preliminary study
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
The budgeted maximum coverage problem
Information Processing Letters
Improving text categorization methods for event tracking
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Populating the Semantic Web by Macro-reading Internet Text
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Towards characterization of actor evolution and interactions in news corpora
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Connecting the dots between news articles
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracing the event evolution of terror attacks from on-line news
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Contextifier: automatic generation of annotated stock visualizations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Finding information is becoming a major part of our daily life. Entire sectors, from Web users to scientists and intelligence analysts, are increasingly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture. In this article, we investigate methods for automatically connecting the dots---providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events starting with the decline of home prices (January 2007), and ending with the health care debate (2009). We formalize the characteristics of a good chain and provide a fast search-driven algorithm to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. We also provide a method to handle partially-specified endpoints, for users who do not know both ends of a story. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate that the objective we propose captures the users’ intuitive notion of coherence, and that our algorithm effectively helps users understand the news.