The choice of the dictionary that provides the possible translations a system has to choose when performing
Cross-Lingual Word Sense Disambiguation (CLWSD) is one of the most important steps in such a task. In this work,
we present a comparison between different dictionaries, in two different frameworks. First of all, a technique
for analysing the potential results of an ideal system using those dictionaries is developed. The second
framework considers the particular unsupervised CLWSD system CO-Graph, and analyses the results obtained when
using different bilingual dictionaries providing the potential translations. Two different CLWSD tasks from the
2010 and 2013 SemEval competitions are used for evaluation, and statistics from the words in the test datasets of
those competitions are studied. The conclusions of the analysis of dictionaries on a particular system lead us to
a proposal that substantially improves the results obtained in that framework. In this proposal a hybrid system
is developed, by combining the results provided by a probabilistic dictionary, and those obtained with a Most
Frequent Sense (MFS) approach. The hybrid approach also outperforms the results obtained by other unsupervised
systems in the considered competitions.