Revealing Prevailing Semantic Contents of Clusters Generated from Untagged Freely Written Text Documents in Natural Languages

Author:

Žižka Jan,Dařena František

Publisher

Springer Berlin Heidelberg

Reference12 articles.

1. Berry, M.W., Kogan, J. (eds.): Text Mining: Applications and Theory. John Wiley & Sons, Chichester (2010)

2. Bloedhorn, S., Blohm, S., Cimiano, P., Giesbrecht, E., Hotho, A., Lösch, U., Mödche, A., Mönch, E., Sorg, P., Staab, S., Völker, J.: Combining Data-Driven and Semantic Approaches for Text Mining. In: Foundations for the Web of Information and Services: A Review of 20 Years of Semantic Web Research, pp. 115–142. Springer, Heidelberg (2011)

3. c5/See5 (June 2013), http://www.rulequest.com/see5-info.html

4. Dařena, F., Žižka, J.: Text Mining-Based Formation of Dictionaries Expressing Opinions in Natural Languages. In: Proceedings of the 17th International Conference on Soft Computing Mendel 2011, Brno, June 15-17, pp. 374–381 (2011)

5. Karypis, G., Zhao, Y.: Criterion Functions for Document Clustering: Experiments and Analysis. Technical Report 01-40, University of Minnesota, USA (2001)

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