Non-Sentential Utterances (NSUs) are short utterances that do not have the form of a full sentence but nevertheless convey a complete sentential meaning in the context of a conversation. NSUs are frequently used to ask follow up questions during interactions with question answer (QA) systems resulting into in-correct answers being presented to their users. Most of the current methods for resolving such NSUs have adopted rule or grammar based approach and have limited applicability. In this paper, we present a data driven statistical method for resolving such NSUs. Our method is based on the observation that humans identify keyword appearing in an NSU and place them in the context of conversation to construct a meaningful sentence. We adapt the keyword to question (K2Q) framework to generate natural language questions using keywords appearing in an NSU and its context. The resulting questions are ranked using different scoring methods in a statistical framework. Our evaluation on a data-set collected using mTurk shows that the proposed method perform significantly better than the previous work that has largely been rule based.