In this paper, we present a new method for learning to finding translations and transliterations on the Web for a given term. The approach involves using a small set of terms and translations to obtain mixed-code snippets from a search engine, and automatically annotating the snippets with tags and features for training a conditional random field model. At runtime, the model is used to extracting translation candidates for a given term. Preliminary experiments and evaluation show our method cleanly combining various features, resulting in a system that outperforms previous work.
More recently, REF ) introduce a method for learning a CRF model to find translations and transliterations of technical terms on the Web.
Learning to Find Translations and Transliterations on the Web
ACL
Computer Science