Paper: Learning to Find Translations and Transliterations on the Web

ACL ID P12-2026
Title Learning to Find Translations and Transliterations on the Web
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

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 run- time, 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.