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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lamirel, J.C., Mall, R., Cuxac, P., Safi, G.: Variations to incremental growing neural gas algorithm based on label maximization. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 956–965 (2011)
Kipper, K., Dang, H.T., Palmer, M.: Class-based construction of a verb lexicon. In: AAAI/IAAI, pp. 691–696 (2000)
Dorr, B.J.: Large-scale dictionary construction for foreign language tutoring and interlingual machine translation. Machine Translation 12(4), 271–325 (1997)
Prescher, D., Riezler, S., Rooth, M.: Using a probabilistic class-based lexicon for lexical ambiguity resolution. In: 18th International Conference on Computational Linguistics, Saarbrucken, Germany, pp. 649–655 (2000)
Korhonen, A.: Semantically motivated subcategorization acquisition. In: ACL Workshop on Unsupervised Lexical Acquisition, Philadelphia (2002)
Sun, L., Korhonen, A., Poibeau, T., Messiant, C.: Investigating the cross-linguistic potential of VerbNet-style classification. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, pp. 1056–1064. Association for Computational Linguistics, Stroudsburg (2010)
Schulte im Walde, S.: Experiments on the automatic induction of german semantic verb classes. Computational Linguistics 32(2), 159–194 (2006)
Kipper Schuler, K.: VerbNet: A Broad-Coverage, Comprehensive Verb Lexicon. PhD thesis, University of Pennsylvania (2006)
Martinetz, T., Schulten, K.: A ”Neural-Gas” Network Learns Topologies. Artificial Neural Networks I, 397–402 (1991)
Fritzke, B.: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems 7, pp. 625–632 (1995)
Prudent, Y., Ennaji, A.: An incremental growing neural gas learns topologies. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 2, pp. 1211–1216 (2005)
Attik, M., Al Shehabi, S., Lamirel, J.C.: Clustering Quality Measures for Data Samples with Multiple Labels. In: Databases and Applications, pp. 58–65 (2006)
Ghribi, M., Cuxac, P., Lamirel, J.C., Lelu, A.: Mesures de qualité de clustering de documents: prise en compte de la distribution des mots clés. In: Béchet, N. (ed.) Évaluation des Méthodes d’Extraction de Connaissances dans les Données, EvalECD 2010, Hammamet, Tunisie, Fatiha Saïs, pp. 15–28 (January 2010)
van den Eynde, K., Mertens, P.: La valence : l’approche pronominale et son application au lexique verbal. Journal of French Language Studies 13, 63–104 (2003)
Kupść, A., Abeillé, A.: Growing TreeLex. In: Gelbukh, A. (ed.) CICLing 2008. LNCS, vol. 4919, pp. 28–39. Springer, Heidelberg (2008)
Gross, M.: Méthodes en syntaxe. Hermann, Paris (1975)
Falk, I., Gardent, C., Lamirel, J.C.: Classifying French Verbs Using French and English Lexical Resources. In: ACL, pp. 207–214 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-35230-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35229-4
Online ISBN: 978-3-642-35230-0
eBook Packages: EngineeringEngineering (R0)