An Unsupervised Alignment Model for Sequence Labeling: Application to Name Transliteration
Najmeh Mousavi Nejad, Shahram Khadivi · 2011
In this paper a new sequence alignment model is proposed for name transliteration systems. In addition, several new features are introduced to enhance the overall accuracy in a name transliteration system. Discriminative methods are used to train the model. Using this model, we achieve improvements on the transliteration accuracy in comparison with the state-of-the-art alignment models. The 1-best name accuracy is also improved using a name selection method from the 10-best list based on the contents of the web. This method leads to a relative improvement of 54 % over 1-best transliteration. The experiments are conducted on an English-Persian name transliteration task. Furthermore, we reproduce the past studies results under the same conditions. Experiments conducting on English to Persian transliteration show that new features provide a relative improvement of 5 % over previous published results. 1