Contextually Self-Organized Maps of Chinese Words
Contextual SOMs of Chinese words have been constructed in this work. Differing from previous approaches, in which individual words were mapped onto the SOM, in this work histograms of various word classes or otherwise defined subsets of words were formed on the SOM array. It was found that the words are not only clustered according to the word classes, but joint or overlapping clusters of words from different classes can also be formed according to the role of the words as sentence constituents. A further new effect was found. When the histograms were formed using test words restricted to certain intervals of word frequencies, the histograms were found to depend on the frequency, and the corresponding partial clusters were often very compact.
KeywordsTarget Word Word Frequency Test Word Chinese Word Text Corpus
Unable to display preview. Download preview PDF.
- 2.Honkela, T., Pulkki, V., Kohonen, T.: Contextual relations of words in Grimm tales, analyzed by self-organizing maps. In: Fogelman-Soulié, F., Gallinari, P. (eds.) Proc. ICANN 1995, Int. Conf. on Artificial Neural Networks, vol. II, pp. 3–7. EC2, Nanterre, France (1995)Google Scholar
- 4.Kohonen, T.: Contextually Self-Organized Maps of Chinese Words. TKK Reports in Information and Computer Science, TKK-ICS-R30. Aalto University School of Science and Technology, Espoo, Finland (2010) (This report is downloadable from ics.tkk.fi/en/research/publications)
- 5.Kohonen, T.: Contextually Self-Organized Maps of Chinese Words. Part II, TKK Reports in Information and Computer Science, TKK-ICS-R35. Aalto University School of Science and Technology, Espoo, Finland (2010) (This report is downloadable from ics.tkk.fi/en/research/publications)
- 6.Sun, H.L., Sun, D.J., Huang, J.P., Li, D.J., Xing, H.B.: Corpus for modern Chinese research. In: Luo, Z.S., Yuan, Y.L. (eds.) Studies in the Chinese language and characters in the era of computers, pp. 283–294. Tsinghua University Press, Beijing, China (1996)Google Scholar
- 7.Vesanto, J., Alhoniemi, E., Himberg, J., Kiviluoto, K., Parviainen, J.: Self-organizing map for data mining in Matlab: the SOM Toolbox. Simulation News Europe 25, 54 (1999) (The SOM Toolbox software package is downloadable from ics.tkk.fi/en/research/software)