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Natural Language Generation (NLG) is the process of generating natural language text from an input, which is a communicative goal and a database or knowledge base. Infor-mally, the architecture of a standard NLG system consists of the following modules (Reiter and Dale, 2000): content determination, sentence planning (or microplanning) and sur-face realization. This thesis is about designing novel, linguistically motivated features for surface realization (the final NLG module mentioned above), the process by which text is created from an abstract representation of language according to the rules of syntax and morphology. It primarily involves three interrelated problems: constituent ordering, in-flection and agreement and function word insertion. For addressing these problems, most state-of-the-art realization ranking models (Velldal and Oepen, 2005; White and Rajkumar, 2009) employ features which are based on very basic insights from linguistic theory (POS tags, rules derived from parse trees, for example). More sophisticated insights of linguis-tic theory have not been widely perceived as necessary for increased system performance, with very basic insights providing the most gains (similar to the situation Johnson (2009