  We explore the use of unsupervised methods in Cross-Lingual Word Sense
Disambiguation (CL-WSD) with the application of English to Persian. Our
proposed approach targets the languages with scarce resources (low-density) by
exploiting word embedding and semantic similarity of the words in context. We
evaluate the approach on a recent evaluation benchmark and compare it with the
state-of-the-art unsupervised system (CO-Graph). The results show that our
approach outperforms both the standard baseline and the CO-Graph system in both
of the task evaluation metrics (Out-Of-Five and Best result).
