@inproceedings{10.1007/978-3-642-15939-8_46,
author={Tristan Snowsill and  Ilias Flaounas and  Tijl De Bie and Nello Cristianini},
title={Detecting Events in a Million New York Times Articles },
booktitle={Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)},
publisher={Springer},
pages={615--618},
month={September},
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 1035263 articles.},
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2001246},
keyword={Artificial Intelligence,Machine Learning},
pubtype={102}
}