String matching is at the core of data cleaning, record matching, and information retrieval. String matching relies on a similarity measure that evaluates the similarity of two strings, regarding the two as a match if their similarity is larger than a user-defined threshold. In our collaboration with journalists and public defenders, we found that real-world datasets, such as police rosters that journalists and public defenders work with, often contain acronyms, abbreviations, and typos, thanks to errors during manual entry, into, say, a spreadsheet or a form. Unfortunately, traditional similarity measures lead to low accuracy since they do not consider all three aspects together. Some recent work proposes leveraging synonym rules to improve matching, but either requires these rules to be provided upfront, or generated prior to matching, which leads to low accuracy in our setting and similar ones. To address these limitations, we propose Smash, a simple yet effective measure to assess the similarity of two strings with acronyms, abbreviations, and typos, all without relying on synonym rules. We design a dynamic programming algorithm to efficiently compute this measure, along with two optimizations that improve accuracy. We show that compared to the best baselines, including one based on ChatGPT with GPT-4, Smash improves the max and mean F-score by 23.5% and 110.8%, respectively. We implement Smash in OpenRefine, a graphical data cleaning tool, to facilitate its use by journalists, public defenders, and other non-programmers for data cleaning.