Summary
During past decade, kernel methods have proved to be successful in different text analysis tasks. There are several reasons that make kernel based methods applicable to many real world problems especially in domains where data is not naturally represented in a vector form. Firstly, instead of manual construction of the feature space for the learning task, kernel functions provide an alternative way to design useful features automatically, therefore, allowing very rich representations. Secondly, kernels can be designed to incorporate a. prior knowledge about the domain. This property allows to notably improve performance of the general learning methods and their simple adaptation to the specific problem. Finally, kernel methods are naturally applicable in situations where data representation is not in a vectorial form, thus avoiding extensive preprocessing step. In this chapter, we present the main ideas behind kernel methods in general and kernels for text analysis in particular as well as provide an example of designing feature space for parse ranking problem with different kernel functions.
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References
Aronszajn, N.: Theory of reproducing kernels. Transactions of the American Mathematical Society 68 (1950)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT, Cambridge, MA (2001)
Herbrich, R.: Learning Kernel Classifiers: Theory and Algorithms. MIT, Cambridge, MA (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York, NY (2004)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: European Conference on Machine Learning (ECML), Berlin, Springer (1998) 137–142
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.J.C.H.: Text classification using string kernels. J. Mach. Learn. Res. 2 (2002) 419–444
Cancedda, N., Gaussier, E., Goutte, C., Renders, J.M.: Word sequence kernels. J. Mach. Learn. Res. 3 (2003) 1059–1082
Haussler, D.: Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, UC Santa Cruz (1999)
Collins, M., Duffy, N.: Convolution kernels for natural language. In Dietterich, T.G., Becker, S., Ghahramani, Z., eds.: NIPS, MIT, Cambridge, MA (2001) 625–632
Gärtner, T., Flach, P.A., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives. In Schölkopf, B., Warmuth, M.K., eds.: Sixteenth Annual Conference on Computational Learning Theory and Seventh Kernel Workshop (COLT-2003). Volume 2777 of Lecture Notes in Computer Science., Springer (2003) 129–143
Pahikkala, T., Tsivtsivadze, E., Boberg, J., Salakoski, T.: Graph kernels versus graph representations: a case study in parse ranking. In Gärtner, T., Garriga, G.C., Meinl, T., eds.: Proceedings of the ECML/PKDD’06 workshop on Mining and Learning with Graphs (MLG’06). (2006)
Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent semantic kernels. J. Intell. Inf. Syst. 18 (2002) 127–152
Leslie, C., Kuang, R.: Fast string kernels using inexact matching for protein sequences. J. Mach. Learn. Res. 5 (2004) 1435–1455
Sleator, D.D., Temperley, D.: Parsing english with a link grammar. Technical Report CMU-CS-91-196, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA (1991)
Tsivtsivadze, E., Pahikkala, T., Boberg, J., Salakoski, T.: Locality-convolution kernel and its application to dependency parse ranking. In Ali, M., Dapoigny, R., eds.: IEA/AIE. Volume 4031 of Lecture Notes in Computer Science., Springer (2006) 610–618
Gärtner, T.: Exponential and geometric kernels for graphs. In: NIPS Workshop on Unreal Data: Principles of Modeling Nonvectorial Data. (2002)
Tsivtsivadze, E., Pahikkala, T., Pyysalo, S., Boberg, J., Mylläri, A., Salakoski, T.: Regularized least-squares for parse ranking. In: Proceedings of the 6th International Symposium on Intelligent Data Analysis, Springer-Verlag (2005) 464–474 Copyright Springer-Verlag Berlin Heidelberg 2005
Lafferty, J., Sleator, D., Temperley, D.: Grammatical trigrams: A probabilistic model of link grammar. In: Proceedings of the AAAI Conference on Probabilistic Approaches to Natural Language, Menlo Park, CA, AAAI Press (1992) 89–97
Pyysalo, S., Ginter, F., Heimonen, J., Björne, J., Boberg, J., Järvinen, J., Salakoski, T.: BioInfer: A corpus for information extraction in the biomedical domain. BMC Bioinformatics (2007) Available at http://www.it.utu.fi/BioInfer.
Kendall, M.G.: Rank Correlation Methods. 4 edn. Griffin, London (1970)
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Tsivtsivadze, E., Pahikkala, T., Boberg, J., Salakoski, T. (2008). Kernels for Text Analysis. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_4
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DOI: https://doi.org/10.1007/978-3-540-78297-1_4
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