Learning better transliterations
Learning better transliterations is a 2009 conference paper written in English by Pasternack J., Roth D. and published in International Conference on Information and Knowledge Management, Proceedings.
We introduce a new probabilistic model for transliteration that performs significantly better than previous approaches, is language-agnostic, requiring no knowledge of the source or target languages, and is capable of both generation (creating the most likely transliteration of a source word) and discovery (selecting the most likely transliteration from a list of candidate words). Our experimental results demonstrate improved accuracy over the existing state-of-the-art by more than 10% in Chinese, Hebrew and Russian. While past work has commonly made use of fixed-size n-gram features along with more traditional models such as HMM or Perceptron, we utilize an intuitive notion of "productions", where each source word can be segmented into a series of contiguous, non-overlapping substrings of any size, each of which independently transliterates to a substring in the target language with a given probability. (e.g. P(wash⇒ BaIII) = 0:95). To learn these parameters, we employ Expectation-Maximization (EM), with the alignment between substrings in the source and target word training pairs as our latent data. Despite the size of the parameter space and the 2
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