A new structure for news editing

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.

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