Clause identification using entropy guided transformation learning

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


Entropy Guided Transformation Learning (ETL) is a machine learning strategy that extends Transformation Based Learning by providing automatic template generation. In this work, we propose an ETL approach to the clause identification task. We use the English language corpus of the CoNLL'2001 shared task. The achieved performance is not competitive yet, since the F β=1 of the ETL based system is 80:55, whereas the state-of-the-art system performance is 85:03. Nevertheless, our modeling strategy is very simple, when compared to the state-of-the-art approaches. These first findings indicate that the ETL approach is a promising one for this task. One can enhance its performance by incorporating problem specific knowledge. Additional features can be easily introduced in the ETL model.

Original languageEnglish
Title of host publication2009 Seventh Brazilian Symposium in Information and Human Language Technology, STIL 2009 : 8 - 11 September 2009, São Carlos, São Paulo, Brazil; Proceedings
Number of pages8
Place of PublicationPiscataway
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date2009
Article number5532445
ISBN (Print)978-1-4244-6008-3
ISBN (Electronic)978-0-7695-3945-4, 978-1-4244-6009-0
Publication statusPublished - 2009
Externally publishedYes
Event7th Brazilian Symposium in Information and Human Language Technology, STIL 2009 - São Carlos, Sao Carlos, Sao Paulo, Brazil
Duration: 08.09.200911.09.2009
Conference number: 7

    Research areas

  • Informatics - Entropy guided transformation learning, clause identification, machine learning, CoNLL'2001 corpus, natural language processing
  • Business informatics