Despite Stack Overflow’s popularity as a resource for solving coding problems, identifying relevant information from an individual post remains a challenge. The overload of information in a post can make it difficult for developers to identify specific and targeted code fixes. In this paper, we aim to help users identify informative code segments, once they have narrowed down their search to a post relevant to their task. Specifically, we explore natural language-based approaches to extract problematic and suggested code pairs from a post. The goal of the study is to investigate the feasibility of designing a browser extension to draw the readers’ attention to relevant code segments, and thus improve the experience of software engineers seeking help on Stack Overflow.