Learning to Find Translations and Transliterations on the Web

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.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Joseph Z. Chang, Jason S. Chang, Jyh-Shing Roger Jang
• Keywords: ACL 2012, Code Snippets, Computational Linguistics, Random Field Model, Translations