Assessing the Quality of Web Content

Elisabeth Lex, Inayat Khan, Horst Bischof, Michael Granitzer

Research output: Contribution to journal › Article › Research › peer-review

Abstract

This paper describes our approach towards the ECML/PKDD Discovery Challenge 2010. The challenge consists of three tasks: (1) a Web genre and facet classification task for English hosts, (2) an English quality task, and (3) a multilingual quality task (German and French). In our approach, we create an ensemble of three classifiers to predict unseen Web hosts whereas each classifier is trained on a different feature set. Our final NDCG on the whole test set is 0:575 for Task 1, 0:852 for Task 2, and 0:81 (French) and 0:77 (German) for Task 3, which ranks second place in the ECML/PKDD Discovery Challenge 2010.
Original languageEnglish
JournalarXiv.org e-Print archive
Publication statusPublished - 12 Jun 2014

Fingerprint

Classifiers

Keywords

  • information quality
  • classification
  • machine learning
  • big data

ASJC Scopus subject areas

  • Computer Science(all)

Fields of Expertise

  • Information, Communication & Computing

Cite this

Assessing the Quality of Web Content. / Lex, Elisabeth; Khan, Inayat; Bischof, Horst; Granitzer, Michael.

In: arXiv.org e-Print archive, 12.06.2014.

Research output: Contribution to journal › Article › Research › peer-review

Lex, E, Khan, I, Bischof, H & Granitzer, M 2014, 'Assessing the Quality of Web Content' arXiv.org e-Print archive.
Lex E, Khan I, Bischof H, Granitzer M. Assessing the Quality of Web Content. arXiv.org e-Print archive. 2014 Jun 12.
Lex, Elisabeth ; Khan, Inayat ; Bischof, Horst ; Granitzer, Michael. / Assessing the Quality of Web Content. In: arXiv.org e-Print archive. 2014.
@article{69c98d954f794f96bf4ac5ec18dfb1c6,
title = "Assessing the Quality of Web Content",
abstract = "This paper describes our approach towards the ECML/PKDD Discovery Challenge 2010. The challenge consists of three tasks: (1) a Web genre and facet classification task for English hosts, (2) an English quality task, and (3) a multilingual quality task (German and French). In our approach, we create an ensemble of three classifiers to predict unseen Web hosts whereas each classifier is trained on a different feature set. Our final NDCG on the whole test set is 0:575 for Task 1, 0:852 for Task 2, and 0:81 (French) and 0:77 (German) for Task 3, which ranks second place in the ECML/PKDD Discovery Challenge 2010.",
keywords = "information quality, classification, machine learning, big data",
author = "Elisabeth Lex and Inayat Khan and Horst Bischof and Michael Granitzer",
note = "4 pages, ECML/PKDD 2010 Discovery Challenge Workshop",
year = "2014",
month = "6",
day = "12",
language = "English",
journal = "arXiv.org e-Print archive",
publisher = "Cornell University Library",

}

TY - JOUR

T1 - Assessing the Quality of Web Content

AU - Lex, Elisabeth

AU - Khan, Inayat

AU - Bischof, Horst

AU - Granitzer, Michael

N1 - 4 pages, ECML/PKDD 2010 Discovery Challenge Workshop

PY - 2014/6/12

Y1 - 2014/6/12

N2 - This paper describes our approach towards the ECML/PKDD Discovery Challenge 2010. The challenge consists of three tasks: (1) a Web genre and facet classification task for English hosts, (2) an English quality task, and (3) a multilingual quality task (German and French). In our approach, we create an ensemble of three classifiers to predict unseen Web hosts whereas each classifier is trained on a different feature set. Our final NDCG on the whole test set is 0:575 for Task 1, 0:852 for Task 2, and 0:81 (French) and 0:77 (German) for Task 3, which ranks second place in the ECML/PKDD Discovery Challenge 2010.

AB - This paper describes our approach towards the ECML/PKDD Discovery Challenge 2010. The challenge consists of three tasks: (1) a Web genre and facet classification task for English hosts, (2) an English quality task, and (3) a multilingual quality task (German and French). In our approach, we create an ensemble of three classifiers to predict unseen Web hosts whereas each classifier is trained on a different feature set. Our final NDCG on the whole test set is 0:575 for Task 1, 0:852 for Task 2, and 0:81 (French) and 0:77 (German) for Task 3, which ranks second place in the ECML/PKDD Discovery Challenge 2010.

KW - information quality

KW - classification

KW - machine learning

KW - big data

M3 - Article

JO - arXiv.org e-Print archive

JF - arXiv.org e-Print archive

ER -

Access to Document