Goal recognition, the task of inferring an agent's goal based on observed action, is an important component of many artificial intelligence applications, including intelligent user interfaces, adversarial systems, and natural language understanding. Most goal recognizers, however, are very slow, which has prevented their use in many practical applications. I will describe my work in using corpus-based machine learning techniques to create goal recognizers that are significantly faster than previous systems.