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- W2136191759 abstract "AbstractWe 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 detectionsuffix treeNew York Times" @default.
- W2136191759 created "2016-06-24" @default.
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- W2136191759 date "2010-01-01" @default.
- W2136191759 modified "2023-09-26" @default.
- W2136191759 title "Detecting Events in a Million New York Times Articles" @default.
- W2136191759 cites W2053915461 @default.
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- W2136191759 doi "https://doi.org/10.1007/978-3-642-15939-8_46" @default.
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