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 anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
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
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
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Towards a context-sensitive online newspaper
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Learning to model relatedness for news recommendation
Proceedings of the 20th international conference on World wide web
Topic chains for understanding a news corpus
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Studying how the past is remembered: towards computational history through large scale text mining
Proceedings of the 20th ACM international conference on Information and knowledge management
Exploring the corporate ecosystem with a semi-supervised entity graph
Proceedings of the 20th ACM international conference on Information and knowledge management
REX: explaining relationships between entity pairs
Proceedings of the VLDB Endowment
Connecting Two (or Less) Dots: Discovering Structure in News Articles
ACM Transactions on Knowledge Discovery from Data (TKDD)
Trains of thought: generating information maps
Proceedings of the 21st international conference on World Wide Web
Learning causality for news events prediction
Proceedings of the 21st international conference on World Wide Web
Connecting the dots between news articles
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Storytelling in entity networks to support intelligence analysts
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering diverse and salient threads in document collections
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Learning to predict from textual data
Journal of Artificial Intelligence Research
Monitoring User Evolution in Twitter
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Reassembling multilingual temporal news datasets with incomplete information
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Information cartography: creating zoomable, large-scale maps of information
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
NIFTY: a system for large scale information flow tracking and clustering
Proceedings of the 22nd international conference on World Wide Web
Finding news story chains based on multi-dimensional event profiles
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
ESTHETE: a news browsing system to visualize the context and evolution of news stories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Story graphs: Tracking document set evolution using dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today's society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly 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 paper, 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 ongoing health-care debate. We formalize the characteristics of a good chain and provide an efficient algorithm (with theoretical guarantees) to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate the algorithm's effectiveness in helping users understanding the news.