Abstract
In this paper we apply the ensemble approach to the identification of incorrectly annotated items (noise) in a training set. In a controlled experiment, memory-based, decision tree-based and transformation-based classifiers are used as a filter to detect and remove noise deliberately introduced into a manually tagged corpus. The results indicate that the method can be successfully applied to automatically detect errors in a corpus.
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Berthelsen, H., Megyesi, B. (2000). Ensemble of Classifiers for Noise Detection in PoS Tagged Corpora. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2000. Lecture Notes in Computer Science(), vol 1902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45323-7_5
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DOI: https://doi.org/10.1007/3-540-45323-7_5
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