Environmental scanning, which acquires and use the information about event, trends, and changes in an organization¡¦s external environment, is an important process in the strategic management of an organization and permits the organization to quickly adapt to the changes of its external environment. Event detection that detects the onset of new events from news documents is essential to facilitating an organization¡¦s environmental scanning activity. However, traditional feature-based event detection techniques detect events by comparing the similarity between features of news stories and incur several problems. For example, for illustration and comparison purpose, a news story may contain sentences or paragraphs that are not highly relevant to defining its event. Without removing such less relevant sentences or paragraphs before detection, the effectiveness of traditional event detection techniques may suffer. In this study, we developed a summary-based event detection (SED) technique that filters less relevant sentences or paragraphs in a news story before performing feature-based event detection. Using a traditional feature-based event detection technique (i.e., INCR) as benchmark, the empirical evaluation results showed that the proposed SED technique could achieve comparable or even better detection effectiveness (measured by miss and false alarm rates) than the INCR technique, for data corpora where the percentage of news stories discussing
old events is high.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0812103-212948 |
| Date | 12 August 2003 |
| Creators | Wu, Pao-Feng |
| Contributors | Chao-Min Chiu, San-Yi Huang, 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-0812103-212948 |
| Rights | off_campus_withheld, Copyright information available at source archive |
Page generated in 0.0016 seconds