Current research in natural language generation
Current research in natural language generation
Being digital
Enriching communities: harbingers of news in the future
IBM Systems Journal
FramerD: representing knowledge in the large
IBM Systems Journal
For want of a bit the user was lost: cheap user modeling
IBM Systems Journal
Calendrical calculations
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
A new structure for news editing
IBM Systems Journal
Justifying imagery: multimedia support for learning through explanation
IBM Systems Journal
Extracting Temporal References to Assign Document Event-Time Periods
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
A new structure for news editing
IBM Systems Journal
Temporal document retrieval model for business news archives
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Clustering of search results using temporal attributes
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
On the value of temporal information in information retrieval
ACM SIGIR Forum
Clustering and exploring search results using timeline constructions
Proceedings of the 18th ACM conference on Information and knowledge management
Review: A framework for awareness maintenance
Journal of Network and Computer Applications
A language modeling approach for temporal information needs
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Influence of timeline and named-entity components on user engagement
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Great strides have been made in the use of computer tools to create, edit, filter, and present information, particularly since the tremendous growth in the mainstream popularity of the World Wide Web. The presence of a computationally rich environment at all stages of news distribution provides a unique opportunity to use these tools to improve the reader experience. Information provided for a general audience from a general source can be combined with small amounts of information specific to a reader to improve the reader's understanding of, connection to, and engagement with the news. This paper discusses Time Frames for extracting time information from news articles. Combining this time information with limited information about the reader, we explore the possibilities for improving the reader experience by augmenting news articles.