Pragmatic language difficulties are common among autistic children, and assessment of pragmatic language skills over time is an important predictor of quality of life outcomes during adulthood. Current metrics for pragmatic language are qualitative in design and are expensive in terms of time and resources. With the use of Natural Language Processing (NLP) methods, robust measures of pragmatic language features can be obtained in an automated, reliable, and relatively inexpensive fashion. Such metrics can be used to augment traditional pragmatic language assessments. Improving our understanding of how autistic individuals use language not only helps us learn how to become better conversational partners ourselves, but also enables us to build language tools that accommodate for pragmatic language differences. In this dissertation, we leverage traditional statistical methods to adapt and augment established NLP techniques to investigate three areas of pragmatic language that autistic children are known to have difficulty with.