An evaluation of phrasal and clustered representations on a text categorization task
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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Language morphology offset: Text classification on a Croatian-English parallel corpus
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Automatic acquisition of inflectional lexica for morphological normalisation
Information Processing and Management: an International Journal
Does dictionary based bilingual retrieval work in a non-normalized index?
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Computers and Graphics
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In this paper we compare n-grams and morphological normalization, two inherently different text-preprocessing methods, used for text classification on a Croatian-English parallel corpus. Our approach to comparing different text preprocessing techniques is based on measuring computational performance (execution time and memory consumption), as well as classification performance. We show that although n-grams achieve classifier performance comparable to traditional word-based feature extraction and can act as a substitute for morphological normalization, they are computationally much more demanding.