Portuguese Part-of-Speech Tagging Using Entropy Guided Transformation Learning
Lecture notes in computer sciencePublished 1 January 2008
Cícero Nogueira dos Santos, Ruy Luiz Milidiú, Raúl P. Renterı́a
Citations34
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SJR score0.35
SNIP0.55
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TL;DR
This work applies the ETL framework to Portuguese Part-of-Speech Taggging and achieves the best results reported so far for Machine Learning based POS tagging of both corpora.
Abstract
Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of Decision Trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to Portuguese Part-of-Speech Taggging. We use two different corpora: Mac-Morpho and Tycho Brahae. ETL achieves the best results reported so far for Machine Learning based POS tagging of both corpora. ETL provides a new training strategy that accelerates transformation learning. For the Mac-Morpho corpus this corresponds to a factor of three speedup. ETL shows accuracies of 96.75% and 96.64% for Mac-Morpho and Tycho Brahae, respectively.
Keywords
Computer Science
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