Paper: Improved Transliteration Mining Using Graph Reinforcement

ACL ID D11-1128
Title Improved Transliteration Mining Using Graph Reinforcement
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

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 mappings are modeled using a bipartite graph. A graph reinforcement algorithm is then used to enrich the graph by inferring additional mappings. During graph reinforcement, appropriate link reweighting is used to promote good mappings and to demote bad ones. The enhanced transliterati...