Detecting Events in a Million New York Times Articles
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.
Keywordsevent detection suffix tree New York Times
Unable to display preview. Download preview PDF.
- 1.Sandhaus, E.: The New York Times Annotated Corpus. In: Linguistic Data Consortium, Philadelphia (2008)Google Scholar
- 2.Snowsill, T., Nicart, F., Stefani, M., De Bie, T., Cristianini, N.: Finding surprising patterns in textual data streams. In: 2010 IAPR Workshop on Cognitive Information Processing (2010)Google Scholar