Works on semiotic dynamics and language as a complex adaptive system in general has been an important lane of research over the last decade. In this study, the mean-field naming game model developed in the course of the pioneering research programme of Luc Steels and colleagues is modified to include probabilistic word choice based on weighted lists of words, instead of either deterministic or totally random word choice based on (ordered) sets of words. The parameters&rsquo / interaction and this interaction&rsquo / s effect on time of convergence of the system and size of individual lexicons over time are investigated. The classical model is found to be a special case of this proposed model. Additionally, this model has more parameters and a larger state space which provides additional room for tweaking for time- or space-optimization of the convergence process.