Clustering tweets using Wikipedia concepts

Guoyu Tang; Yunqing Xia; Weizhi Wang; Raymond Lau; Thomas Fang Zheng

Clustering tweets using Wikipedia concepts

Guoyu Tang, Yunqing Xia, Weizhi Wang, Raymond Lau, Thomas Fang Zheng

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent research which indicates Wikipedia is promising in representing text, we exploit Wikipedia concepts in representing tweets with concept vectors. We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections. To further alleviate the sparse data problem, we further make use of three types of out-links in Wikipedia. Evaluation on a twitter dataset shows that the concept model outperforms the traditional VSM model in tweet clustering.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14)
Publisher	European Language Resources Association (ELRA)
Pages	2262-2267
ISBN (Print)	9782951740884
Publication status	Published - May 2014
Event	9th International Conference on Language Resources and Evaluation (LREC 2014) - Harpa Concert Hall and Conference Center, Reykjavik, Iceland Duration: 26 May 2014 → 31 May 2014 http://www.lrec-conf.org/proceedings/lrec2014/index.html

Conference

Conference	9th International Conference on Language Resources and Evaluation (LREC 2014)
Place	Iceland
City	Reykjavik
Period	26/05/14 → 31/05/14
Internet address	http://www.lrec-conf.org/proceedings/lrec2014/index.html

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

Tweet clustering
Tweet representation
Wikipedia concept

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@inproceedings{b028ebacf11e4edc9ddd5864524e5616,

title = "Clustering tweets using Wikipedia concepts",

abstract = "Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent research which indicates Wikipedia is promising in representing text, we exploit Wikipedia concepts in representing tweets with concept vectors. We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections. To further alleviate the sparse data problem, we further make use of three types of out-links in Wikipedia. Evaluation on a twitter dataset shows that the concept model outperforms the traditional VSM model in tweet clustering.",

keywords = "Tweet clustering, Tweet representation, Wikipedia concept",

author = "Guoyu Tang and Yunqing Xia and Weizhi Wang and Raymond Lau and Zheng, {Thomas Fang}",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 9th International Conference on Language Resources and Evaluation (LREC 2014) ; Conference date: 26-05-2014 Through 31-05-2014",

year = "2014",

month = may,

language = "English",

isbn = "9782951740884",

pages = "2262--2267",

booktitle = "Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14)",

publisher = "European Language Resources Association (ELRA)",

url = "http://www.lrec-conf.org/proceedings/lrec2014/index.html",

}

Clustering tweets using Wikipedia concepts. / Tang, Guoyu; Xia, Yunqing; Wang, Weizhi et al.
Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14). European Language Resources Association (ELRA), 2014. p. 2262-2267.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Clustering tweets using Wikipedia concepts

AU - Tang, Guoyu

AU - Xia, Yunqing

AU - Wang, Weizhi

AU - Lau, Raymond

AU - Zheng, Thomas Fang

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2014/5

Y1 - 2014/5

N2 - Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent research which indicates Wikipedia is promising in representing text, we exploit Wikipedia concepts in representing tweets with concept vectors. We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections. To further alleviate the sparse data problem, we further make use of three types of out-links in Wikipedia. Evaluation on a twitter dataset shows that the concept model outperforms the traditional VSM model in tweet clustering.

AB - Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent research which indicates Wikipedia is promising in representing text, we exploit Wikipedia concepts in representing tweets with concept vectors. We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections. To further alleviate the sparse data problem, we further make use of three types of out-links in Wikipedia. Evaluation on a twitter dataset shows that the concept model outperforms the traditional VSM model in tweet clustering.

KW - Tweet clustering

KW - Tweet representation

KW - Wikipedia concept

UR - http://www.scopus.com/inward/record.url?scp=84975873471&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84975873471&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9782951740884

SP - 2262

EP - 2267

BT - Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14)

PB - European Language Resources Association (ELRA)

T2 - 9th International Conference on Language Resources and Evaluation (LREC 2014)

Y2 - 26 May 2014 through 31 May 2014

ER -

Clustering tweets using Wikipedia concepts

Abstract

Conference

Bibliographical note

Research Keywords

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