A constraint approach to lexicon induction for low-resource languages

Mairidan Wushouer*, Donghui Lin, Toru Ishida, Yohei Murakami

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


Bilingual lexicon is a useful language resource, but such data rarely available for lower-density language pairs, especially for those that are closely related. The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction becomes a difficult task. Using a third language to link two other languages is a well-known solution in low-resource situation, which usually requires only two input bilingual lexicons to automatically induce the new one. This approach, however, is weak in measuring semantic distance between bilingual word pairs because it has never been demonstrated to utilize the complete structures of the input bilingual lexicons as dropped meanings negatively influence the result. This research discuss a constraint approach to pivot-based lexicon induction in case the target language pair are closely related. We create constraints from language similarity and model the structures of the input dictionaries as an optimization problem whose solution produces optimally correct target bilingual lexicon. In addition, we enable created bilingual lexicons of low-resource languages accessible through service grid federation.

Original languageEnglish
Title of host publicationCognitive Technologies
PublisherSpringer Verlag
Number of pages15
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameCognitive Technologies
ISSN (Print)1611-2482


  • Bilingual dictionary induction
  • Constraint satisfaction problem
  • Low-resource languages
  • Pivot language
  • Weighted partial max-SAT

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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