Improved Transliteration Mining Using Graph Reinforcement

  title={Improved Transliteration Mining Using Graph Reinforcement},
  author={Ali El Kahki and Kareem Darwish and Ahmed Saad El Din and Mohamed Abd El-Wahab and Ahmed Hefny and Waleed Ammar},
Mining of transliterations from comparable or parallel text can enhance natural language processing applications such as machine translation and cross language information retrieval. This paper presents an enhanced transliteration mining technique that uses a generative graph reinforcement model to infer mappings between source and target character sequences. An initial set of mappings are learned through automatic alignment of transliteration pairs at character sequence level. Then, these… CONTINUE READING
Highly Cited
This paper has 27 citations. REVIEW CITATIONS

Similar Papers

Loading similar papers…