Name
Abstract | ||
|---|---|---|
Environmental scanning, the acquisition and use of the information about events, trends, and relationships in an organization's external environment, permits an organization to adapt to its environment and to develop effective responses to secure or improve the organization's position in the future, Event detection technique that identifies the onset of new events from streams of news stories would facilitate the process of organization's environmental scanning. However, traditional event detection techniques generally adopted the feature co-occurrence approach that identifies whether a news story contains an unseen event by comparing the similarity of features between the new story and past news stories. Such feature-based event detection techniques greatly suffer from the word mismatch and inconsistent orientation problems and do not directly support event categorization and news stories filtering. In this study, we developed an information extraction-based event detection (NEED) technique that combines information extraction and text categorization techniques to address the problems inherent to traditional feature-based event detection techniques. Using a traditional feature-based event detection technique (i.e., INCR) as benchmarks, the empirical evaluation results showed that the proposed NEED technique improved the effectiveness of event detection measured by the tradeoff between miss and false alarm rates. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/S0167-9236(03)00028-9 | Decision Support Systems |
Keywords | Field | DocType |
event detection,event tracking,event categorization,traditional feature-based event detection,news story,event detection technique,unseen event,online news document,new event,environmental scanning,information extraction,feature-based event detection technique,text categorization,information extraction-based event detection,traditional event detection technique,false alarm rate | Categorization,Data mining,False alarm,Information retrieval,Computer science,Filter (signal processing),Complex event processing,Information extraction,Text categorization,Event tracking | Journal |
Volume | Issue | ISSN |
36 | 4 | Decision Support Systems |
Citations | PageRank | References |
30 | 1.17 | 27 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-ping Wei | 1 | 743 | 74.20 |
| Yen-hsien Lee | 2 | 118 | 16.64 |