Distributional Thesaurus Versus WordNet: A Comparison of Backoff Techniques for Unsupervised PP Attachment

  • Hiram Calvo
  • Alexander Gelbukh
  • Adam Kilgarriff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3406)


Prepositional Phrase (PP) attachment can be addressed by considering frequency counts of dependency triples seen in a non-annotated corpus. However, not all triples appear even in very big corpora. To solve this problem, several techniques have been used. We evaluate two different backoff methods, one based on WordNet and the other on a distributional (automatically created) thesaurus. We work on Spanish. The thesaurus is created using the dependency triples found in the same corpus used for counting the frequency of unambiguous triples. The training corpus used for both methods is an encyclopaedia. The method based on a distributional thesaurus has higher coverage but lower precision than the WordNet method.


Natural Language Processing Similar Word Computational Linguistics Prepositional Phrase Backoff Algorithm 
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 2005

Authors and Affiliations

  • Hiram Calvo
    • 1
  • Alexander Gelbukh
    • 1
  • Adam Kilgarriff
    • 2
  1. 1.Center for Computing ResearchNational Polytechnic InstituteMexico
  2. 2.Lexical Computing Ltd.United Kingdom

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