Empirical methods for compound splitting

  • Authors:
  • Philipp Koehn;Kevin Knight

  • Affiliations:
  • University of Southern California;University of Southern California

  • Venue:
  • EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
  • Year:
  • 2003

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Abstract

Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We evaluate them against a gold standard and measure their impact on performance of statistical MT systems. Results show accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a German-English noun phrase translation task.