A constraint approach to lexicon induction for low-resource languages

Mairidan Wushouer, Donghui Lin, Toru Ishida, Yohei Murakami

研究成果: Chapter


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.

ホスト出版物のタイトルCognitive Technologies
出版社Springer Verlag
出版ステータスPublished - 2018


名前Cognitive Technologies

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

フィンガープリント 「A constraint approach to lexicon induction for low-resource languages」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。