Event detection from online news documents for supporting environmental scanning

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 new 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.