Skip to Main Content
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 machine-derived 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 clas s of applications that make use of article augmentation.
Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.