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.
CITATION STYLE
Berthelsen, H., & Megyesi, B. (2000). Ensemble of classifiers for noise detection in pos tagged corpora. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1902, pp. 27–32). Springer Verlag. https://doi.org/10.1007/3-540-45323-7_5
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