The ability to identify what humans are doing in the environment is a crucial element of successful responsive behavior in human-robot interaction. We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that comprise plans. However, NLP commonly treats text as a bag-of-words, omitting such structural relationships and using topic models to break down the distribution of concepts discussed in documents. In this paper, we examine an analogous treatment of plans as distributions of activities. We explore the application of Latent Dirichlet Allocation topic models to human skeletal data of plan execution traces obtained from a RGB-D sensor. This investigation focuses on representing the data as text and interpreting learned activities as a form of activity recognition (AR). Additionally, we explain how the system may perform PR. The initial empirical results suggest that such NLP methods can be useful in complex PR and AR tasks.