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  Combining Multiple Classifiers to Improve Part of Speech Tagging: A Case Study for Brazilian Portuguese (2000) [1 citations — 0 self]

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by Rachel V. Xavier Aires, Ra M. Aluísio, Denise C. S. Kuhn, Marcio L. B, Osvaldo N. Oliveira
In the Proceedings of the Brazilian AI Symposium (SBIA’2000
http://www.nilc.icmc.usp.br/nilc/download/AiresSBIA2000.ps
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Abstract:

Abstract. Four taggers have been trained on a 100,000-word corpus of Brazilian Portuguese, namely Unigram (Treetagger), N-gram (Treetagger), transformationbased (TBL) and Maximum-Entropy tagging (MXPOST). The latter displayed the best accuracy (88.73%), which is still much lower than the state-of-the-art accuracy for English. The low accuracy is attributed to the reduced size of the training corpus. Twelve methods of combination were used, four of which led to an improvement over the MXPOST accuracy. The best result (89.42%) was obtained with a majority-wins voting strategy. 1

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