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Detecting Events in a Million New York Times Articles.
- Source :
- Machine Learning & Knowledge Discovery in Databases (9783642159381); 2010, p615-618, 4p
- Publication Year :
- 2010
-
Abstract
- 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 1 035 263 articles. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783642159381
- Database :
- Complementary Index
- Journal :
- Machine Learning & Knowledge Discovery in Databases (9783642159381)
- Publication Type :
- Book
- Accession number :
- 76773414
- Full Text :
- https://doi.org/10.1007/978-3-642-15939-8_46