Detecting events in a million New York Times articles

TM Snowsill; I Flaounas; Bie Tijl De; Nello Cristianini

Detecting events in a million New York Times articles

TM Snowsill, I Flaounas, Bie Tijl De, Nello Cristianini

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

4 Citations (Scopus)

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.

Translated title of the contribution	Detecting events in a million New York Times articles
Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)
Publisher	Springer
Publication status	Published - Oct 2010

Bibliographical note

Other page information: 615-618
Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)
Other identifier: 2001246

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@inproceedings{17d1b0f06b8a4ab893fe9e3c84140587,

title = "Detecting events in a million New York Times articles",

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.",

author = "TM Snowsill and I Flaounas and {Tijl De}, Bie and Nello Cristianini",

note = "Other page information: 615-618 Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) Other identifier: 2001246",

year = "2010",

month = oct,

language = "English",

booktitle = "Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)",

publisher = "Springer",

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AU - Snowsill, TM

AU - Flaounas, I

AU - Tijl De, Bie

AU - Cristianini, Nello

N1 - Other page information: 615-618 Conference Proceedings/Title of Journal: Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) Other identifier: 2001246

PY - 2010/10

Y1 - 2010/10

N2 - 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.

AB - 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.

M3 - Conference Contribution (Conference Proceeding)

BT - Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)

PB - Springer

ER -

Detecting events in a million New York Times articles

Abstract

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