Abstract
Clustering was usually applied on numerical and categorical information. However, textual information is acquiring an increasing importance with the appearance of methods for textual data mining. This paper proposes the use of classical clustering algorithms with a mixed function that combines numerical, categorical and semantic features. The content of the semantic features is extracted from textual data. This paper analyses and compares the behavior of different existing semantic similarity functions that use WordNet as background ontology. The different partitions obtained with the clustering algorithm are compared to human classifications in order to see which one approximates better the human reasoning. Moreover, the interpretability of the obtained clusters is discussed. The results show that those similarity measures that provide better results when compared using a standard benchmark also provide better and more interpretable partitions.
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References
Xu, R., Wunsch, D.I.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Hotho, A., Maedche, A., Staab, S.: Ontology-based text document clustering. Künstliche Intelligenz 4, 48–54 (2002)
Batet, M., Valls, A., Gibert, K.: Improving classical clustering with ontologies. In: Proceedings of the 4th World conference of the IASC, Japan, pp. 137–146 (2008)
Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proc. 32nd annual Meeting of the Association for Computational Linguistics, USA, pp. 133–138 (1994)
Leacock, C., Chodorow, M.: Combining local context and wordnet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An electronic lexical database, pp. 265–283. MIT Press, Cambridge (1998)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proc. of 14th Int’l Joint Conf. on Artificial Intelligence, pp. 448–453 (1995)
Lin, D.: An information-theoretic definition of similarity. In: Proc. of the 15th International Conference on Machine Learning, Madison, USA, pp. 296–304 (1998)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Int’l Conf. on Research in Computational Linguistics, pp. 19–33 (1997)
Miller, G., Charles, W.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1–28 (1991)
Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998) More information, http://www.cogsci.princeton.edu/~wn/
Rada, R., Mili, H., Bichnell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 9(1), 17–30 (1989)
Batet, M., Sánchez, D., Valls, A., Gibert, K.: Ontology-based semantic similarity in the biomedical domain. In: 12th Conf. on Artificial Intelligence in Medicine. WS. Intelligent Data Analysis in Biomedicine and Pharmacology, Italy, pp. 41–46 (2009)
López de Mántaras, R.: A distance-based attribute selection measure for decision tree induction. Machine learning 6, 81–92 (1991)
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Batet, M., Valls, A., Gibert, K. (2010). Performance of Ontology-Based Semantic Similarities in Clustering. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_36
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DOI: https://doi.org/10.1007/978-3-642-13208-7_36
Publisher Name: Springer, Berlin, Heidelberg
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