Abstract
The IGNGF (Incremental Growing Neural Gas with Feature maximisation) method is a recent neural clustering method in which the use of a standard distance measure for determining a winner is replaced in IGNGF by cluster feature maximization. One main advantage of this method as compared to concurrent methods is that the maximized features used during learning can also be exploited in a final step for accurately labeling the resulting clusters. In this paper, we apply this method to the unsupervised classification of French verbs. We evaluate the obtained clusters (i.e., verb classes) in three different ways. The first one relies on an usual gold standard, the second one on unsupervised cluster quality indexes and the last one on a qualitative analysis. Our experiment illustrates that, conversely to former approaches for automatically acquiring verb classes, IGNGF method permits to produce relevant verb classes and to accurately associate the said classes with an explicit characterisation of the syntactic and semantic properties shared by the classes elements.
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Lamirel, JC., Falk, I., Gardent, C. (2013). Enhancing NLP Tasks by the Use of a Recent Neural Incremental Clustering Approach Based on Cluster Data Feature Maximization. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_24
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DOI: https://doi.org/10.1007/978-3-642-35230-0_24
Publisher Name: Springer, Berlin, Heidelberg
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