Experiments with Dynamic Topic Models

Jinjing Li; Wray Buntine

Experiments with Dynamic Topic Models

Jinjing Li
, Wray Buntine

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

General summary of news content is a task under the general heading of “information summarisation,” and it is recognised as a way of overcoming information overload. How does one summarise a large number of articles with their time as well as topical content? Here we introduce one technique, dynamic topic models, built using discrete non-parametric techniques, and demonstrate our software that can do this relatively efficiently using multi-core methods. Examples are used from a 760k collection of news articles from the Australian Broadcasting Commission (ABC) website over a ten year period.

Original language	English
Title of host publication	Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Place of Publication	United States
Publisher	Association for Computing Machinery (ACM)
Pages	1-5
Number of pages	5
ISBN (Print)	9781450329569
Publication status	Published - 2014
Event	NewsKDD 2014: Data Science for News Publishing - New York, New York, United States Duration: 24 Aug 2014 → 27 Aug 2014 https://www.kdd.org/kdd2014/workshops.html

Conference

Conference	NewsKDD 2014
Abbreviated title	NewsKDD 2014
Country/Territory	United States
City	New York
Period	24/08/14 → 27/08/14
Internet address	https://www.kdd.org/kdd2014/workshops.html

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@inproceedings{f7188eac630b416bbc69f098673ea50a,

title = "Experiments with Dynamic Topic Models",

abstract = "General summary of news content is a task under the general heading of “information summarisation,” and it is recognised as a way of overcoming information overload. How does one summarise a large number of articles with their time as well as topical content? Here we introduce one technique, dynamic topic models, built using discrete non-parametric techniques, and demonstrate our software that can do this relatively efficiently using multi-core methods. Examples are used from a 760k collection of news articles from the Australian Broadcasting Commission (ABC) website over a ten year period.",

author = "Jinjing Li and Wray Buntine",

year = "2014",

language = "English",

isbn = "9781450329569",

pages = "1--5",

booktitle = "Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "NewsKDD 2014 : Data Science for News Publishing, NewsKDD 2014 ; Conference date: 24-08-2014 Through 27-08-2014",

url = "https://www.kdd.org/kdd2014/workshops.html",

}

Li, J & Buntine, W 2014, Experiments with Dynamic Topic Models. in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. Association for Computing Machinery (ACM), United States, pp. 1-5, NewsKDD 2014, New York, New York, United States, 24/08/14. <https://pdfs.semanticscholar.org/13d7/e84cba296b30ba5dffb805f7ef0ab41ad7c0.pdf>

TY - GEN

T1 - Experiments with Dynamic Topic Models

AU - Li, Jinjing

AU - Buntine, Wray

PY - 2014

Y1 - 2014

N2 - General summary of news content is a task under the general heading of “information summarisation,” and it is recognised as a way of overcoming information overload. How does one summarise a large number of articles with their time as well as topical content? Here we introduce one technique, dynamic topic models, built using discrete non-parametric techniques, and demonstrate our software that can do this relatively efficiently using multi-core methods. Examples are used from a 760k collection of news articles from the Australian Broadcasting Commission (ABC) website over a ten year period.

AB - General summary of news content is a task under the general heading of “information summarisation,” and it is recognised as a way of overcoming information overload. How does one summarise a large number of articles with their time as well as topical content? Here we introduce one technique, dynamic topic models, built using discrete non-parametric techniques, and demonstrate our software that can do this relatively efficiently using multi-core methods. Examples are used from a 760k collection of news articles from the Australian Broadcasting Commission (ABC) website over a ten year period.

M3 - Conference contribution

SN - 9781450329569

SP - 1

EP - 5

BT - Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

PB - Association for Computing Machinery (ACM)

CY - United States

T2 - NewsKDD 2014

Y2 - 24 August 2014 through 27 August 2014

ER -

Experiments with Dynamic Topic Models

Abstract

Conference

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