A topic detection and tracking system with TF-density

Shu Wei Liu; Hsien Tsung Chang

doi:10.1007/978-3-642-28807-4_17

A topic detection and tracking system with TF-density

Shu Wei Liu^*, Hsien Tsung Chang

^*Corresponding author for this work

Chang Gung University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In the past, news consumption took place predominantly via newspapers and were hard to track. Nowadays, the rapid growth of the Internet means that news are continually being shared and stored on a previously unimaginable scale. It is now possible to access several news stories on the same topic on a single web page. In this paper, we proposed a topic detection and tracking system with a new word measurement scheme named TF-Density. TF-Density is a new algorithm modified from the well-known TF-IWF and TF-IDF algorithms to provide a more precise and efficient method to recognize the important words in the text. Through our experiments, we demonstrated that our proposed topic detection and tracking system is capable of providing more precise and convenient result for the tracking of news by users.

Original language	English
Title of host publication	Recent Progress in Data Engineering and Internet Technology
Pages	115-120
Number of pages	6
Edition	VOL. 1
DOIs	https://doi.org/10.1007/978-3-642-28807-4_17
State	Published - 2013
Event	International Conference on Data Engineering and Internet Technology, DEIT 2011 - Bali, Indonesia Duration: 15 03 2012 → 17 03 2012

Publication series

Name	Lecture Notes in Electrical Engineering
Number	VOL. 1
Volume	156 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Data Engineering and Internet Technology, DEIT 2011
Country/Territory	Indonesia
City	Bali
Period	15/03/12 → 17/03/12

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10.1007/978-3-642-28807-4_17

Cite this

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title = "A topic detection and tracking system with TF-density",

abstract = "In the past, news consumption took place predominantly via newspapers and were hard to track. Nowadays, the rapid growth of the Internet means that news are continually being shared and stored on a previously unimaginable scale. It is now possible to access several news stories on the same topic on a single web page. In this paper, we proposed a topic detection and tracking system with a new word measurement scheme named TF-Density. TF-Density is a new algorithm modified from the well-known TF-IWF and TF-IDF algorithms to provide a more precise and efficient method to recognize the important words in the text. Through our experiments, we demonstrated that our proposed topic detection and tracking system is capable of providing more precise and convenient result for the tracking of news by users.",

author = "Liu, {Shu Wei} and Chang, {Hsien Tsung}",

year = "2013",

doi = "10.1007/978-3-642-28807-4_17",

language = "英语",

isbn = "9783642288067",

series = "Lecture Notes in Electrical Engineering",

number = "VOL. 1",

pages = "115--120",

booktitle = "Recent Progress in Data Engineering and Internet Technology",

edition = "VOL. 1",

note = "International Conference on Data Engineering and Internet Technology, DEIT 2011 ; Conference date: 15-03-2012 Through 17-03-2012",

}

Liu, SW & Chang, HT 2013, A topic detection and tracking system with TF-density. in Recent Progress in Data Engineering and Internet Technology. VOL. 1 edn, Lecture Notes in Electrical Engineering, no. VOL. 1, vol. 156 LNEE, pp. 115-120, International Conference on Data Engineering and Internet Technology, DEIT 2011, Bali, Indonesia, 15/03/12. https://doi.org/10.1007/978-3-642-28807-4_17

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AB - In the past, news consumption took place predominantly via newspapers and were hard to track. Nowadays, the rapid growth of the Internet means that news are continually being shared and stored on a previously unimaginable scale. It is now possible to access several news stories on the same topic on a single web page. In this paper, we proposed a topic detection and tracking system with a new word measurement scheme named TF-Density. TF-Density is a new algorithm modified from the well-known TF-IWF and TF-IDF algorithms to provide a more precise and efficient method to recognize the important words in the text. Through our experiments, we demonstrated that our proposed topic detection and tracking system is capable of providing more precise and convenient result for the tracking of news by users.

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A topic detection and tracking system with TF-density

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