Abstract
In this article, we use the model adjectives using a vector space model. We further employ three different dimension reduction methods, the Principal Component Analysis (PCA), the Self-Organizing Map (SOM), and the Neighbor Retrieval Visualizer (NeRV) in the projection and visualization task, using antonym test for evaluation. The results show that while the results between the three methods are comparable, the NeRV performs best of the three, and all of them are able to preserve meaningful information for further analysis.
This work has been supported by the Academy of Finland and the Finnish Funding Agency for Technology and Innovation.
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References
Firth, J.R.: Papers in linguistics 1934-1951. Oxford University Press, Oxford (1957)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)
Venna, J., Peltonen, J., Nybo, K., Aidos, H., Kaski, S.: Information retrieval perspective to nonlinear dimensionality reduction for data visualization
Honkela, T., Pulkki, V., Kohonen, T.: Contextual relations of words in Grimm tales, analyzed by self-organizing map. In: Proceedings of ICANN 1995, Nanterre, France, vol. II, pp. 3–7. EC2 (1995)
Lagus, K., Airola, A.: Semantic clustering of verbs – analysis of morphosyntactic contexts using the SOM algorithm. In: Acquisition and Representation of Word Meaning: Theoretical and computational perspectives, Pisa, Roma. Linguistica Computazionale. Instituti Editoriali E Poligrafici Internazionali, vol. XXII-XXIII, pp. 263–287 (2005)
Honkela, T.: Adaptive and holistic knowledge representations using self-organizing maps. In: Proceedings of Int. Conference on Intelligent Information Processing, IIP 2000, pp. 81–86. IFIP (2000)
Schütze, H.: Word Space. In: Advances in Neural Information Processing Systems, NIPS Conference, vol. 5, pp. 895–902. Morgan Kaufmann Publishers Inc., San Francisco (1993); Journal of Machine Learning Research 11, 451–490 (2010)
Venna, J., Kaski, S.: Nonlinear dimensionality reduction as information retrieval. In: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 (2007)
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Honkela, T., Lindh-Knuutila, T., Lagus, K. (2010). Measuring Adjective Spaces. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_46
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DOI: https://doi.org/10.1007/978-3-642-15819-3_46
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