Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus

@article{Mehdi2017ExcavatingTM,
  title={Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus},
  author={Mohamad Mehdi and Chitu Okoli and Mostafa Mesgari and Finn {\AA}rup Nielsen and Arto Lanam{\"a}ki},
  journal={Inf. Process. Manag.},
  year={2017},
  volume={53},
  pages={505-529}
}
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22 Citations
Background Citations
9
Methods Citations
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22 Citations

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Citation Type

Testing the validity of Wikipedia categories for subject matter labelling of open-domain corpus data
TLDR
The results suggest that at least the taxonomy derived from the Wikipedia category system is not a valid instrument for manual subject matter labelling of open-domain text corpora.
Use of Wikipedia categories on information retrieval research: a brief review
TLDR
This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research, and shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.
How can wikipedia be used to support the process of automatically building multilingual domain modules? a case study
Exploring the Domain of Information “Users”: Semantic Analysis of Wikipedia Articles
TLDR
The findings reveal that Wikipedia covers various topics of the information users domain, ranging from information search behavior, information retrieval, human-computer interaction, user experience, human factors, and to others.
How Wikipedia disease information evolve over time? An analysis of disease-based articles changes
Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries
Title Computing controversy : Formal model and algorithms fordetecting controversy on Wikipedia and in search queries
TLDR
A formal model of controversy is introduced as the basis of computational approaches to detecting controversial concepts and a classification based method for automatic detection of controversial articles and categories in Wikipedia is proposed.
Computing semantic similarity based on novel models of semantic representation using Wikipedia
Open semantic analysis: The case of word level semantics in Danish
TLDR
Data-driven models for Danish semantic relatedness, word intrusion and sentiment prediction are described and it is found that logistic regression and large random forests perform well with semantic representations.
Evaluation of Naive Bayes and Support Vector Machines for Wikipedia
TLDR
This work compares and illustrates the effectiveness of two standard classifiers in the text classification literature, Naive Bayes and Support Vector Machines, on the full English Wikipedia corpus for six different categories, and shows that SVM (linear kernel) performs exceptionally across all categories.
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