In this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual
Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual
dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most
suitable translations from the dictionary. Different algorithms that combine both the dictionary and the
co-occurrence graph are then used for performing this selection of the final translations: techniques based

on sub-graphs (communities) containing clusters of words with related meanings, based on distances between
nodes representing words, and based on the relative importance of each node in the whole graph. The initial
output of the system is enhanced with translation probabilities, provided by a statistical bilingual
dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 20
10, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed
and compared to those obtained by the systems participating in the competitions. Our system offers the best
results in comparison with other unsupervised systems in most of the experiments, and even overcomes
supervised systems in some cases.