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A Hybrid Approach for Multiword Expression Identification

  • Carlos Ramisch
  • Helena de Medeiros Caseli
  • Aline Villavicencio
  • André Machado
  • Maria José Finatto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6001)

Abstract

Considerable attention has been given to the problem of Multiword Expression (MWE) identification and treatment, for NLP tasks like parsing and generation, to improve the quality of results. Statistical methods have been often employed for MWE identification, as an inexpensive and language independent way of finding co-occurrence patterns. On the other hand, more linguistically motivated methods for identification, which employ information such as POS filters and lexical alignment between languages, can produce more targeted candidate lists. In this paper we propose a hybrid approach that combines the strenghts of different sources of information using a machine learning algorithm to produce more robust and precise results. Automatic evaluation on gold standards shows that the performance of our hybrid method is superior to the individual results of statistical and alignment-based MWE extraction approaches for Portuguese and for English. This method can be used to aid lexicographic work by providing a more targeted MWE candidate list.

Keywords

Bayesian Network Machine Translation Candidate List Parallel Corpus Pointwise Mutual Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Carlos Ramisch
    • 1
    • 2
  • Helena de Medeiros Caseli
    • 3
  • Aline Villavicencio
    • 2
    • 4
  • André Machado
    • 2
  • Maria José Finatto
    • 5
  1. 1.GETALP/LIGUniversity of Grenoble(France)
  2. 2.Institute of InformaticsFederal University of Rio Grande do Sul(Brazil)
  3. 3.Department of Computer ScienceFederal University of São Carlos(Brazil)
  4. 4.Department of Computer SciencesBath University(UK)
  5. 5.Institute of Language and LinguisticsFederal University of Rio Grande do Sul(Brazil)

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