Language Impairment in Alzheimer's disease can occur because of deficits in semantic levels of language processing. It can be studied using computational models of language such as complex semantic networks which are strongly related to semantic memory. We hypothesize that the concept of resilience in scale-free semantic networks can truly model and predict semantic language deficit in Alzheimer's disease. We suggest that increasing the variety of words in the lexicon of patients with Alzheimer's disease, improves the resilience of their semantic networks through breakdowns. Moreover, enlarging the size of the semantic networks of patients with Alzheimer's disease can make these networks more resilient.