2 [ 99] [Ramachandran 01] sound symbolism[hinton 95] [ 06] Ueda et al.[ueda 12] I [ 93] SVM [ 12, Aramaki 12] SVM 3 Twitter ,8
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1 Which sense does an onomatopoeia belong to? 1 1 1,2 Tetsuaki Nakamura 1 Mai Miyabe 1 Eiji Aramaki 1,2 1 1 Unit of Design, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University 2 2 JST PRESTO Abstract: This study aims to develop a system which visualizes subjective information. Focusing on onomatopoeias as such information, we estimate which senses an onomatopoeia belongs to among touch, taste, smell, hearing, sight, pleasure (positive) and unpleasure (negative). For this purpose, we use a machine learning method (Support Vector Machine) which utilizes phonetic symbols and the number of occurrences of them in the onomatopoeia. Then, the experimental result for evaluation demonstrates that (1) the best performance is achieved for hearing and sight, and (2) the performance of the classifier is similar to that of human. Finally, we propose the system which creates city maps displaying distribution of subjective information for senses. 1 [Houghton 12] [ 11] [Suzuki 11] tetsuakinakamura8@gmail.com 1 kyoshinmonitor.html Support Vector MachineSVM[Cortes 95]
2 2 [ 99] [Ramachandran 01] sound symbolism[hinton 95] [ 06] Ueda et al.[ueda 12] I [ 93] SVM [ 12, Aramaki 12] SVM 3 Twitter ,817, [ 06, Ueda 12] : : a y i r u w e N o Q k R s D t P n Y h W m v todkitodki kyunkyun kirkirhopahopa [ 99] $bigram trigram
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6 9: κ : κ SVM κ A i.e., BC D synesthetic metaphor / synaesthetic metaphor [Ullmann 51, Williams 76, Yu 03, Werning 06, 94] [ 12] A D κ Twitter 1 7 SVM SVM SVM JST
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8 [Aramaki 12] Aramaki, E., Yasuda, S., Miyabe, M., Miura, S., and Murata, M.: Which is Stronger? : Discriminative Learning of Sound Symbolism, in Proceedings of the 34th Annual Meeting of the Cognitive Science Society (CogSci2012), p (2012) [Cortes 95] Cortes, C. and Vapnik, V.: Support- Vector Networks, Machine Learning, Vol. 20, No. 3, pp (1995) [ 06],,, 2,, Vol. 62, No. 11, pp (2006) [Hinton 95] Hinton, L., Nichols, J., and Ohala, J. J. eds.: Sound Symbolism, Cambridge University Press, Cambridge (1995) [Houghton 12] Houghton, A., Prudent, N., Scott III, J. E., Wade, R., and Luber, G.: Climate Change-Related Vulnerabilities and Local Environmental Public Health Tracking through GEMSS: A Web-Based Visualization Tool, Applied Geography, Vol. 33, pp (2012) [ 94],, (1994) Communications Technology, Vol. 58, No. 3, pp (2011) [ 99],,, (1999) [Ueda 12] Ueda, Y., Shimizu, Y., and Sakamoto, M.: System Construction Supporting Communication with Foreign Doctors Using Onomatopoeia Expressing Pains, in Proceedings of the 6th International Conference of Soft Computing and Intelligent System, pp (2012) [Ullmann 51] Ullmann, S.: The principles of semantics, Blackwell, Oxford (1951) [Werning 06] Werning, M., Fleischhauer, J., and Beseoglu, H.: The cognitive accessibility of synaesthetic metaphors, in Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp (2006) [Williams 76] Williams, J. M.: Synaesthetic adjectives: A possible law of semantic change, Language, Vol. 52, No. 2, pp (1976) [Yu 03] Yu, N.: Synesthetic metaphor: a cognitive perspective, Journal of literaty semantics, Vol. 32, No. 1, pp (2003) [ 12],,,,, 18, pp (2012) [ 93],, Vol. 49, No. 9, pp (1993) [ 12],,,, Vol. 19, No. 3, pp (2012) [Ramachandran 01] Ramachandran, V. S. and Hubbard, E. M.: Synaesthesia: A window into perception, thought and language, Journal of Consciousness Studies, Vol. 8, No. 12, pp (2001) [ 11],,, (VENUS),, Vol. 46, No. 1, pp (2011) [Suzuki 11] Suzuki, M. and Inoue, D.: DAEDALUS: Practical Alert System Based on Large-scale Darknet Monitoring for Protecting Live Networks, Journal of the National Institute of Information and
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