1. Deniz Yuret and Ergun Biçici. Modeling Morphologically Rich Languages Using Split Words and Unstructured Dependencies. In ACL-IJCNLP 2009 Conf. Short Papers, Suntec, Singapore, pages 345-348, 8 2009. [WWW] [PDF] [POSTSCRIPT] Keyword(s): Language Modeling. Abstract:

   We experiment with splitting words into their stem and suffix components for modeling morphologically rich languages. We show that using a morphological analyzer and disambiguator results in a significant perplexity reduction in Turkish. We present flexible n-gram models, FlexGrams, which assume that the n-1 tokens that determine the probability of a given token can be chosen anywhere in the sentence rather than the preceding n-1 positions. Our final model achieves 27\% perplexity reduction compared to the standard n-gram model.

   
   

   @InProceedings{yuret-bicici:2009:Short,
   author = {Deniz Yuret and Ergun Bi\c{c}ici},
   title = {Modeling Morphologically Rich Languages Using Split Words and Unstructured Dependencies},
   booktitle = {ACL-IJCNLP 2009 Conf. Short Papers},
   month = {8},
   year = {2009},
   address = {Suntec, Singapore},
   pages = {345--348},
   url = {http://www.aclweb.org/anthology/P/P09/P09-2087},
   keywords = {Language Modeling},
   pdf = {bicici.github.io/publications/2009/MMRLACL.pdf},
   ps = {bicici.github.io/publications/2009/MMRLACL.ps},
   abstract = {We experiment with splitting words into their stem and suffix components for modeling morphologically rich languages. We show that using a morphological analyzer and disambiguator results in a significant perplexity reduction in Turkish. We present flexible n-gram models, FlexGrams, which assume that the n-1 tokens that determine the probability of a given token can be chosen anywhere in the sentence rather than the preceding n-1 positions. Our final model achieves 27\% perplexity reduction compared to the standard n-gram model.},
   
   }
   

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