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Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities

Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities

František Dařena, Jan Žižka
Copyright: © 2017 |Pages: 40
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch081
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MLA

Dařena, František, and Jan Žižka. "Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1981-2020. https://doi.org/10.4018/978-1-5225-1759-7.ch081

APA

Dařena, F. & Žižka, J. (2017). Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1981-2020). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch081

Chicago

Dařena, František, and Jan Žižka. "Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1981-2020. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch081

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Abstract

The chapter introduces clustering as a family of algorithms that can be successfully used to organize text documents into groups without prior knowledge of these groups. The chapter also demonstrates using unsupervised clustering to group large amount of unlabeled textual data (customer reviews written informally in five natural languages) so it can be used later for further analysis. The attention is paid to the process of selecting clustering algorithms, their parameters, methods of data preprocessing, and to the methods of evaluating the results by a human expert with an assistance of computers, too. The feasibility has been demonstrated by a number of experiments with external evaluation using known labels and expert validation with an assistance of a computer. It has been found that it is possible to apply the same procedures, including clustering, cluster validation, and detection of topics and significant words for different natural languages with satisfactory results.

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