Learning features through feedback for blog distillation

Dehong Gao; Renxian Zhang; Wenjie Li; Yiu Keung Lau; Kam Fai Wong

doi:10.1145/2009916.2010061

Learning features through feedback for blog distillation

Dehong Gao, Renxian Zhang, Wenjie Li, Yiu Keung Lau, Kam Fai Wong

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.

Original language	English
Title of host publication	SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages	1085-1086
Number of pages	2
DOIs	https://doi.org/10.1145/2009916.2010061
Publication status	Published - 1 Sept 2011
Event	34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 - Beijing, China Duration: 24 Jul 2011 → 28 Jul 2011

Conference

Conference	34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11
Country/Territory	China
City	Beijing
Period	24/07/11 → 28/07/11

Keywords

Blog distillation
Faceted distillation
Feedback

ASJC Scopus subject areas

Information Systems

More information

10.1145/2009916.2010061

Cite this

@inproceedings{d01abc0219c34d7895dfa0fd115054c4,

title = "Learning features through feedback for blog distillation",

abstract = "The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.",

keywords = "Blog distillation, Faceted distillation, Feedback",

author = "Dehong Gao and Renxian Zhang and Wenjie Li and Lau, {Yiu Keung} and Wong, {Kam Fai}",

year = "2011",

month = sep,

day = "1",

doi = "10.1145/2009916.2010061",

language = "English",

isbn = "9781450309349",

pages = "1085--1086",

booktitle = "SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval",

note = "34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 ; Conference date: 24-07-2011 Through 28-07-2011",

}

Gao, D, Zhang, R, Li, W, Lau, YK & Wong, KF 2011, Learning features through feedback for blog distillation. in SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1085-1086, 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11, Beijing, China, 24/07/11. https://doi.org/10.1145/2009916.2010061

Learning features through feedback for blog distillation. / Gao, Dehong; Zhang, Renxian; Li, Wenjie et al.
SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011. p. 1085-1086.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Gao, Dehong

AU - Zhang, Renxian

AU - Li, Wenjie

AU - Lau, Yiu Keung

AU - Wong, Kam Fai

PY - 2011/9/1

Y1 - 2011/9/1

N2 - The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.

AB - The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.

KW - Blog distillation

KW - Faceted distillation

KW - Feedback

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U2 - 10.1145/2009916.2010061

DO - 10.1145/2009916.2010061

M3 - Conference article published in proceeding or book

SN - 9781450309349

SP - 1085

EP - 1086

BT - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

T2 - 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11

Y2 - 24 July 2011 through 28 July 2011

ER -

Learning features through feedback for blog distillation

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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