Since the inception of Talmy's (1985) theory of event conflation and his subsequent typology based on it (Talmy, 1995), many studies have been conducted attempting to refine the typology, place languages within it, and observe its effects on second language acquisition and cognition. One common type of study is based on observing large bodies of text and finding the number of satellite- and verb-framed expressions. However, due to shifts in the definitions of the terminology and sometimes difficult to distinguish nature of conflated events, it can be easy to miss potential examples of event-conflation in long texts or miscategorize them, which can affect the results. Since there are still many studies of conflated events conducted by single authors and others where multiple authors discuss coding but leave finding the events to a single person, we posit that an automated tool that helps to search for and categorize conflated events can help researchers to make more accurate counts and judgments of these events. This study presents such a tool that we created to identify and categorize conflated events and results that underscore its importance. Specifically, we attempted to analyze three publicly available texts and found that at least one of the two authors missed nearly half of the event conflation examples in the texts. Though the number that both authors missed was much lower, the tool did not miss any of these examples and thus can provide a clearly critical second check. Furthermore, we found that though the tool tends to slightly over-identify conflated events, it does so in a way that provides percentages of satellite-framed expressions similar to the combined efforts of the two authors (3% margin of error; F1-score .8958). This is critical because percentages of satellite-framed expressions are often used as the metric for inclusion in one area of Talmy's (1991) typology or another.