Open Access

Portuguese corpus-based learning using ETL

  • Ruy Luiz Milidiú1,
  • Cícero Nogueira dos Santos1 and
  • Julio Cesar Duarte1, 2
Journal of the Brazilian Computer Society14:BF03192569

Received: 22 July 2008

Accepted: 30 November 2008


We present Entropy Guided Transformation Learning models for three Portuguese Language Processing tasks: Part-of-Speech Tagging, Noun Phrase Chunking and Named Entity Recognition. For Part-of-Speech Tagging, we separately use the Mac-Morpho Corpus and the Tycho Brahe Corpus. For Noun Phrase Chunking, we use the SNR-CLIC Corpus. For Named Entity Recognition, we separately use three corpora: HAREM, MiniHAREM and LearnNEC06.

For each one of the tasks, the ETL modeling phase is quick and simple. ETL only requires the training set and no handcrafted templates. ETL also simplifies the incorporation of new input features, such as capitalization information, which are sucessfully used in the ETL based systems. Using the ETL approach, we obtain state-of-the-art competitive performance in all six corpora-based tasks. These results indicate that ETL is a suitable approach for the construction of Portuguese corpus-based systems.


Entropy Guided Transformation Learningtransformation-based learningdecision trees naturallanguage processing