2016 QualityAssessmentofWikipediaArt

• (Dang & Ignat, 2016) ⇒ Quang Vinh Dang, and Claudia-Lavinia Ignat. (2016). “Quality Assessment of Wikipedia Articles Without Feature Engineering.” In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries. ISBN:9781450342292 DOI:10.1145/2910896.2910917

Subject Headings: Text Error Correction System, Text Wikification System, Disambiguation To Wikipedia System, Deep Neural Network, Spelling Error Correction Task, Knowledge Representation.

Notes

• Article Versions and URLs:
  • ACM DL: 10.1145/2910896.2910917.
  • DBLP: jcdl/DangI16.
  • IEEE: 7559560.

Cited By

• Google Scholar: ~ 29 Citations
• Semantic Scholar: ~ 17 Citations.

2020

• (Melli et al., 2020) ⇒ Gabor Melli, Abdelrhman Eldallal, Bassim Lazem, and Olga Moreira. (2020). “GM-RKB WikiText Error Correction Task and Baselines.”. In: Proceedings of LREC 2020 (LREC-2020).

2017

• (Dang & Ignat, 2017) ⇒ Quang-Vinh Dang, and Claudia-Lavinia Ignat. (2017). “An End-to-end Learning Solution for Assessing the Quality of Wikipedia Articles.” In: Proceedings of the 13th International Symposium on Open Collaboration, pp. 1-10.

Quotes

Keywords

• Quality Assessment, Feature Engineering, Wikipedia, Document Representation, Deep Learning

Abstract

As Wikipedia became the largest human knowledge repository, quality measurement of its articles received a lot of attention during the last decade. Most research efforts focused on classification of Wikipedia articles quality by using a different feature set. However, so far, no “golden feature set” was proposed. In this paper, we present a novel approach for classifying Wikipedia articles by analysing their content rather than by considering a feature set. Our approach uses recent techniques in natural language processing and deep learning, and achieved a comparable result with the state-of-the-art

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 QualityAssessmentofWikipediaArt	Quang Vinh Dang Claudia-Lavinia Ignat			Quality Assessment of Wikipedia Articles Without Feature Engineering				10.1145/2910896.2910917