Environmental scanning is an important process, which acquires and uses the information about events, trends, and relationships in an organization's external environment. It permits an organization to adapt to its environment and to develop effective responses to secure or improve their 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 feature-based event detection techniques, which identify whether a news story contains an unseen event by comparing the similarity of words between the news story and past news stories, incur some limitations (e.g., the features shown in news document cannot actually represent the event described in it.). Thus, 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. The empirical evaluation results showed that the NEED technique outperformed the traditional feature-based event detection techniques in miss rate and false alarm rate and achieved comparable event association accuracy rate to its counterpart.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0730100-014038 |
| Date | 30 July 2000 |
| Creators | Lee, Yen-Hsien |
| Contributors | Fu-Ren Lin, San-Yih Hwang, Chih-Ping Wei |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730100-014038 |
| Rights | unrestricted, Copyright information available at source archive |
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