Transliteration mining is aimed at building high quality multi-lingual named entity (NE) lexicons for improving performance in various Natural Language Processing (NLP) tasks including Machine Translation (MT) and Cross Language Information Retrieval (CLIR). In this paper, we apply two Dynamic Bayesian network (DBN)-based edit distance (ED) approaches in mining transliteration pairs from Wikipedia. Transliteration identification results on standard corpora for seven language pairs suggest that the DBN-based edit distance approaches are suitable for modeling transliteration similarity. An evaluation on mining transliteration pairs from English-Hindi and English-Tamil Wikipedia topic pairs shows that they improve transliteration mining quality over state-of-the-art approaches.
['Peter Nabende']
Mining Transliterations from Wikipedia using Dynamic Bayesian Networks