This article advocates a particular type of semi-automated approach to working with corpus data termed “shared evaluation”, the central idea of which is that the computer takes over more of the work of sorting and classifying the data, while a subsequent pass by a human coder ensures the ultimate accuracy of the data selection and classification. The article begins with a discussion of the traditional approach to corpus data and the tools that are currently available. It then describes the shared evaluation approach and compares this to a typical concordancer-based approach. The article goes on to present SVEP, a computer program developed by the authors to implement this approach and offered freely to other researchers, describing the most significant aspects of the program and its use. A case study involving adjective complementation is then presented, including examples of how SVEP was used in the study and an evaluation of the accuracy the program achieved. The article ends with a discussion of the advantages and disadvantages of SVEP in particular (and some ways the program might be improved) and of semi-automated approaches such as shared evaluation in general.