The society of mind
Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Information extraction as a basis for high-precision text classification
ACM Transactions on Information Systems (TOIS)
Media banks: entertainment and the Internet
IBM Systems Journal
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
Salient stills: process and practice
IBM Systems Journal
Matrix computations (3rd ed.)
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
A Framework for Representing Knowledge
A Framework for Representing Knowledge
The search for meaning in large text databases
The search for meaning in large text databases
Time frames: temporal augmentation of the news
IBM Systems Journal
Information fusion for spoken document retrieval
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Automated Personalization of Internet News
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Time frames: temporal augmentation of the news
IBM Systems Journal
A metaphor for personalized television programming
ERCIM'02 Proceedings of the User interfaces for all 7th international conference on Universal access: theoretical perspectives, practice, and experience
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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 natural-language 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.