A research team in the late 1990s is tasked with building a system to automatically categorize a massive, newly digitized library of one million news articles into topics like 'Sports', 'Politics', and 'Business'. The team has a very limited budget, allowing them to hire an expert to manually label only 500 articles. Given the constraints and the nature of the task, which of the following approaches represents the most historically successful and pragmatic strategy for them to pursue?

Match each early Natural Language Processing task with the description that best illustrates how a model could be improved using its own predictions on unlabeled data.

Comparative Analysis of an Iterative Labeling Technique