Topic tracking based on keywords dependency profile

Wei Zheng; Yu Zhang; Yu Hong; Jili Fan; Ting Liu

doi:10.1007/978-3-540-68636-1_13

Topic tracking based on keywords dependency profile

Wei Zheng^*
, Yu Zhang
, Yu Hong
, Jili Fan
, Ting Liu

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

7 Scopus citations

Abstract

Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is "known" by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.

Original language	English
Title of host publication	Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers
Pages	129-140
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-540-68636-1_13
State	Published - 2008
Externally published	Yes
Event	4th Asia Information Retrieval Symposium, AIRS 2008 - Harbin, China Duration: 15 Jan 2008 → 18 Jan 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4993 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th Asia Information Retrieval Symposium, AIRS 2008
Country/Territory	China
City	Harbin
Period	15/01/08 → 18/01/08

Keywords

Keywords dependency profile
Topic detection and tracking
Topic tracking
Word co-occurrence

Access to Document

10.1007/978-3-540-68636-1_13

Cite this

Zheng, W., Zhang, Y., Hong, Y., Fan, J., & Liu, T. (2008). Topic tracking based on keywords dependency profile. In Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers (pp. 129-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4993 LNCS). https://doi.org/10.1007/978-3-540-68636-1_13

@inproceedings{a12c03d254a84dc0966ba59e3380efae,

title = "Topic tracking based on keywords dependency profile",

abstract = "Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is {"}known{"} by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25\% on training dataset and 7.49\% on testing dataset comparing to baseline.",

keywords = "Keywords dependency profile, Topic detection and tracking, Topic tracking, Word co-occurrence",

author = "Wei Zheng and Yu Zhang and Yu Hong and Jili Fan and Ting Liu",

year = "2008",

doi = "10.1007/978-3-540-68636-1\_13",

language = "英语",

isbn = "3540686339",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "129--140",

booktitle = "Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers",

note = "4th Asia Information Retrieval Symposium, AIRS 2008 ; Conference date: 15-01-2008 Through 18-01-2008",

}

Zheng, W, Zhang, Y, Hong, Y, Fan, J & Liu, T 2008, Topic tracking based on keywords dependency profile. in Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4993 LNCS, pp. 129-140, 4th Asia Information Retrieval Symposium, AIRS 2008, Harbin, China, 15/01/08. https://doi.org/10.1007/978-3-540-68636-1_13

Topic tracking based on keywords dependency profile. / Zheng, Wei; Zhang, Yu; Hong, Yu et al.
Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. 2008. p. 129-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4993 LNCS).

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

TY - GEN

T1 - Topic tracking based on keywords dependency profile

AU - Zheng, Wei

AU - Zhang, Yu

AU - Hong, Yu

AU - Fan, Jili

AU - Liu, Ting

PY - 2008

Y1 - 2008

N2 - Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is "known" by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.

AB - Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is "known" by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.

KW - Keywords dependency profile

KW - Topic detection and tracking

KW - Topic tracking

KW - Word co-occurrence

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DO - 10.1007/978-3-540-68636-1_13

M3 - 会议稿件

AN - SCOPUS:45449111528

SN - 3540686339

SN - 9783540686330

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers

T2 - 4th Asia Information Retrieval Symposium, AIRS 2008

Y2 - 15 January 2008 through 18 January 2008

ER -

Zheng W, Zhang Y, Hong Y, Fan J, Liu T. Topic tracking based on keywords dependency profile. In Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers. 2008. p. 129-140. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-68636-1_13

Topic tracking based on keywords dependency profile

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