Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.