We present a demonstration of a newly developed text stream event detection method on over a million articles from the New York Times corpus. The event detection is designed to operate in a predominantly on-line fashion, reporting new events within a specified timeframe. The event detection is achieved by detecting significant changes in the statistical properties of the text where those properties are efficiently stored and updated in a suffix tree. This particular demonstration shows how our method is effective at discovering both short- and long-term events (which are often denoted topics), and how it automatically copes with topic drift on a corpus of 1035263 articles.
|Translated title of the contribution||Detecting events in a million New York Times articles|
|Title of host publication||Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)|
|Publication status||Published - Oct 2010|
Bibliographical noteOther page information: 615-618
Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)
Other identifier: 2001246