Abstract
The polarity classification task has as objective to automatically deciding whether a subjective text is positive or negative. Using a cross-domain setting implies the use of different domains for the training and testing. Recently, string kernels, a method which does not employ domain adaptation techniques has been proposed. In this work, we analyse the performance of this method across four different languages: English, German, French and Japanese. Experimental results show the strong potential of this approach independently from the language.
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Acknowledgements
The work of the third author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).
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Giménez-Pérez, R.M., Franco-Salvador, M., Rosso, P. (2018). String Kernels for Polarity Classification: A Study Across Different Languages. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_50
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DOI: https://doi.org/10.1007/978-3-319-91947-8_50
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