Using Wikipedia with associative networks for document classification

Niels Bloom; Mariet Theune; Franciska M.G. de Jong

Using Wikipedia with associative networks for document classification

Niels Bloom, Mariet Theune, Franciska M.G. de Jong

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

2 Citations (Scopus)

41 Downloads (Pure)

Abstract

We demonstrate a new technique for building associative networks based on Wikipedia, comparing them to WordNet-based associative networks that we used previously, nding the Wikipedia-based networks to perform better at document classification. Additionally, we compare the performance of associative networks to various other text classification techniques using the Reuters-21578 dataset, establishing that associative networks can achieve comparable results.

Original language	Undefined
Title of host publication	Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
Place of Publication	Bruges
Publisher	ESANN
Pages	47-52
Number of pages	6
ISBN (Print)	978-2-87419-081-0
Publication status	Published - 24 Apr 2013
Event	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium Duration: 24 Apr 2013 → 26 Apr 2013

Publication series

Name
Publisher	ESANN

Conference

Conference	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
Period	24/04/13 → 26/04/13
Other	24-26 April 2013

Keywords

EWI-24137
METIS-302578
IR-88564

Access to Document

http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-106.pdf

Cite this

@inproceedings{61459f2ef4d94093bd60fccb015b556e,

title = "Using Wikipedia with associative networks for document classification",

abstract = "We demonstrate a new technique for building associative networks based on Wikipedia, comparing them to WordNet-based associative networks that we used previously, nding the Wikipedia-based networks to perform better at document classification. Additionally, we compare the performance of associative networks to various other text classification techniques using the Reuters-21578 dataset, establishing that associative networks can achieve comparable results.",

keywords = "EWI-24137, METIS-302578, IR-88564",

author = "Niels Bloom and Mariet Theune and {de Jong}, {Franciska M.G.}",

year = "2013",

month = apr,

day = "24",

language = "Undefined",

isbn = "978-2-87419-081-0",

publisher = "ESANN",

pages = "47--52",

booktitle = "Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013",

note = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 ; Conference date: 24-04-2013 Through 26-04-2013",

}

Bloom, N, Theune, M & de Jong, FMG 2013, Using Wikipedia with associative networks for document classification. in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. ESANN, Bruges, pp. 47-52, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, 24/04/13. <http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-106.pdf>

Using Wikipedia with associative networks for document classification. / Bloom, Niels; Theune, Mariet; de Jong, Franciska M.G.
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges: ESANN, 2013. p. 47-52.

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

TY - GEN

T1 - Using Wikipedia with associative networks for document classification

AU - Bloom, Niels

AU - Theune, Mariet

AU - de Jong, Franciska M.G.

PY - 2013/4/24

Y1 - 2013/4/24

N2 - We demonstrate a new technique for building associative networks based on Wikipedia, comparing them to WordNet-based associative networks that we used previously, nding the Wikipedia-based networks to perform better at document classification. Additionally, we compare the performance of associative networks to various other text classification techniques using the Reuters-21578 dataset, establishing that associative networks can achieve comparable results.

AB - We demonstrate a new technique for building associative networks based on Wikipedia, comparing them to WordNet-based associative networks that we used previously, nding the Wikipedia-based networks to perform better at document classification. Additionally, we compare the performance of associative networks to various other text classification techniques using the Reuters-21578 dataset, establishing that associative networks can achieve comparable results.

KW - EWI-24137

KW - METIS-302578

KW - IR-88564

M3 - Conference contribution

SN - 978-2-87419-081-0

SP - 47

EP - 52

BT - Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

PB - ESANN

CY - Bruges

T2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

Y2 - 24 April 2013 through 26 April 2013

ER -