(1 + ε)-approximate sparse recovery
Eric Price, David P. Woodruff
FOCS 2011
Ideally a computational approach could assist in the human-intensive tasks associated with selecting and presenting timely, relevant information, i.e., news editing. At present this goal is difficult to achieve because of the paucity of effective machine-understanding systems for news. A structure for news that affords a fluid interchange between human and machinederived expertise is a step toward improving both the efficiency and utility of on-line news. This paper examines a system that employs richer representations of texts within a corpus of news. These representations are composed by a collection of experts who examine news articles in the database, looking at both the text itself and the annotations placed by other experts. These experts employ a variety of methods ranging from statistical examination to naturallanguage parsing to query expansion through specific-purpose knowledge bases. The system provides a structure for the sharing of knowledge with human editors and the development of a class of applications that make use of article augmentation. © 2000 IBM.
Eric Price, David P. Woodruff
FOCS 2011
Chidanand Apté, Fred Damerau, et al.
ACM Transactions on Information Systems (TOIS)
Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
Marshall W. Bern, Howard J. Karloff, et al.
Theoretical Computer Science