This paper describes the TextLearner prototype, a knowledge-acquisition program that represents the culmination of the DARPA-IPTO-sponsored Reading Learning Comprehension seedling program, an effort to determine the feasibility of autonomous knowledge acquisition through the analysis of text. Built atop the Cyc Knowledge Base and implemented almost entirely in the formal representation language of CycL, TextLearner is an anomaly in the way of Natural Language Understanding programs. The system operates by generating an information-rich model of its target document, and uses that model to explore learning opportunities. In particular, TextLearner generates and evaluates hypotheses, not only about the content of the target document, but about how to interpret unfamiliar natural language constructions. This paper focuses on this second capability and describes four algorithms TextLearner uses to acquire rules for interpreting text.