Abstract
Our aim is to automatically classify psychomimes such as ukiuki and wakuwaku, which appear frequently in Japanese daily life. This task is important because psychomimes represent users’ emotions and have multiple meanings with various contexts. Our previous study focused on these characteristics using fuzzy c-means algorithms. However, only one data set tended to be arranged near a centroid, with the other data located away from the centroid of its group. This means it is difficult to regard the second psychomime as belonging to the same group. This arrangement might have resulted from the vector space description, which used 879 content words to form a high-dimensional vector. Therefore, we attempt two solutions to reduce the dimensionality, namely the adoption of similarity descriptions, such as the cosine similarity, and the application of vector quantization algorithms to our data. We conduct two experiments on psychomime classification using these procedures, and compare the results.
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Kurosawa, Y., Mera, K., Takezawa, T.: Psychomime Classification and Visualization Using a Self-Organizing Map for Implementing Emotional Spoken Dialog System. In: Minker, W., Lee, G.G., Nakamura, S., Mariani, J. (eds.) Spoken Dialogue Systems Technology and Design, pp. 107–134. Springer (2010)
Asaga, C., Yusuf, M., Watanabe, C.: Examinations of Clustering Technique for Classifying Sentences by Meaning of Onomatopoeia on Online Onomatopoeia Example-based Dictionary. DBSJ Letters 6(2) (2007) (in Japanese)
Komiya, K., Kotani, Y.: Classification of Japanese Onomatopoeias using Hierarchical Clustering Depending on Contexts. In: International Joint Conference on Computer Science and Software Engineering, pp. 108–113 (2011)
Kurosawa, Y., Hatamoto, N., Hamada, S., Takezawa, T.: Comparing Clustering Algorithms for Psychomime Classification using Probabilistic Latent Semantic Analysis and Fuzzy c-Means. In: Advances in Knowledge-Based and Intelligent Information and Engineering Systems, pp. 565–574 (2012)
Kohonen, T.: Self-Organizing Map, 3rd Extended edn. Springer, Berlin (2001)
Gath, I., Geva, A.B.: Unsupervised Optimal Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 773–781 (1989)
Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Transactions on Communications 28(1), 84–95 (1980)
Zhang, S., Wang, R.S., Zhang, X.S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374, 483–490 (2007)
Suzuki, Y., Fukumoto, F.: Classifying Japanese Polysemous Verbs based on Fuzzy C-means Clustering. In: Graph-based Methods for Natural Language Processing, pp. 32–40 (2009)
Fukumoto, F., Suzuki, Y., Yamashita, K.: Polysemous Verb Classification Using Sub-categorization Acquisition and Graph-Based Clustering. In: Vetulani, Z. (ed.) LTC 2009. LNCS, vol. 6562, pp. 115–126. Springer, Heidelberg (2011)
Hashimoto, W., Nakamura, T., Miyamoto, S.: Comparison and Evaluation of Different Cluster Validity Measures Including Their Kernelization. Journal of Advanced Computational Intelligence and Intelligent Informatics 13(3), 204–209 (2009)
Miyamoto, S.: On Usefulness of Methods of Fuzzy Clustering. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 21(6), 1008–1017 (2009) (in Japanese)
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Kurosawa, Y., Takezawa, T., Pham, T.D. (2014). Psychomime Classification Using Similarity Measures and Fuzzy c-Means. In: Pham, T.D., Ichikawa, K., Oyama-Higa, M., Coomans, D., Jiang, X. (eds) Biomedical Informatics and Technology. ACBIT 2013. Communications in Computer and Information Science, vol 404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54121-6_17
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DOI: https://doi.org/10.1007/978-3-642-54121-6_17
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