This thesis proposes a new rule based approach to automatic question generation. The proposed approach focuses on analysis of both syntactic and semantic structure of a sentence. The design and implementation of the proposed approach are also explained in detail. Although the primary objective of the designed system is question generation from sentences, automatic evaluation results shows that, it also achieves great performance on reading comprehension datasets, which focus on question generation from paragraphs. With respect to human evaluations, the designed system significantly outperforms all other systems and generated the most natural (human-like) questions.