As the spread of information has received a compelling boost due to pervasive use of social media, so has the spread of misinformation. The sheer volume of data has rendered the traditional methods of expert-driven manual fact-checking largely infeasible. As a result, computational linguistics and data-driven algorithms have been explored in recent years. Despite this progress, identifying and prioritizing what needs to be checked has received little attention. Given that expert-driven manual intervention is likely to remain an important component of fact-checking, especially in specific domains (e.g., politics, environmental science), this identification and prioritization is critical. A successful algorithmic ranking of “check-worthy” claims can help an expert-in-the-loop fact-checking system, thereby reducing the expert’s workload while still tackling the most salient bits of misinformation. In this work, we explore how linguistic syntax, semantics, and the contextual meaning of words play a role in determining the check-worthiness of claims. Our preliminary experiments used explicit stylometric features and simple word embeddings on the English language dataset in the Check-worthiness task of the CLEF-2018 Fact-Checking Lab, where our primary solution outperformed the other systems in terms of the mean average precision, R-precision, reciprocal rank, and precision at k for multiple values k. Here, we present an extension of this approach with more sophisticated word embeddings and report further improvements in this task.