Statistical models for unsupervised, semi-supervised, and supervised transliteration mining

Hassan Sajjad, Helmut Schmid, Alexander Fraser, Hinrich Schütze

Research output: Contribution to journalArticle

3 Citations (Scopus)


We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the transliteration submodel learns to generate transliteration pairs and the fixed non-transliteration model generates the noise pairs. After training, the unlabeled data is disambiguated based on the posterior probabilities of the two sub-models. We evaluate our transliteration mining system on data from a transliteration mining shared task and on parallel corpora. For three out of four language pairs, our system outperforms all semi-supervised and supervised systems that participated in the NEWS 2010 shared task. On word pairs extracted from parallel corpora with fewer than 2% transliteration pairs, our system achieves up to 86.7% F-measure with 77.9% precision and 97.8% recall.

Original languageEnglish
Pages (from-to)350-375
Number of pages26
JournalComputational Linguistics
Issue number2
Publication statusPublished - 1 Jun 2017


ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications
  • Artificial Intelligence

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