Compiling a massive, multilingual dictionary via probabilistic inference

  • Authors:
  • Mausam;Stephen Soderland;Oren Etzioni;Daniel S. Weld;Michael Skinner;Jeff Bilmes

  • Affiliations:
  • University of Washington, Seattle;University of Washington, Seattle;University of Washington, Seattle;University of Washington, Seattle;Google, Seattle;University of Washington, Seattle

  • Venue:
  • ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
  • Year:
  • 2009

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Abstract

Can we automatically compose a large set of Wiktionaries and translation dictionaries to yield a massive, multilingual dictionary whose coverage is substantially greater than that of any of its constituent dictionaries? The composition of multiple translation dictionaries leads to a transitive inference problem: if word A translates to word B which in turn translates to word C, what is the probability that C is a translation of A? The paper introduces a novel algorithm that solves this problem for 10,000,000 words in more than 1,000 languages. The algorithm yields PanDictionary, a novel multilingual dictionary. PanDictionary contains more than four times as many translations than in the largest Wiktionary at precision 0.90 and over 200,000,000 pairwise translations in over 200,000 language pairs at precision 0.8.