
\section{Introduction}
\label{sec:introduction}
\input{introduction}

\section{Unsupervised CL-WSD Method}
\label{sec:methodology}
\input{methodology}

\section{Experiments Setup}
\label{sec:setup}
\input{setup}

\section{Results and Discussion}
\label{sec:experiments}
\input{results}

\section{Conclusion}
\label{sec:conclusion}
We study the application of word embedding-based methods in unsupervised Cross Language Word Sense Disambiguation (CL-WSD) when translating an English noun, appeared in a paragraph, to Persian. Our semantic approach uses embedding of the candidate translations as well as translated context terms to calculate the semantic similarity of each translation to its context. The proposed approach outperforms both the CO-Graph system---a state-of-the-art system in unsupervised CL-WSD---as well as the standard baseline.

We however observe fundamental limitations of the methods based exclusively on context as bag of words. Despite this fact, the current work offers a possible solution for all languages/domains with scarce knowledge-based or parallel corpora resources, by exploiting the use of a monolingual corpus together with a simple bilingual lexicon.


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