Extracting knowledge from unstructured text is a long-standing goal of NLP. Al- though learning approaches to many of its subtasks have been developed (e.g., pars- ing, taxonomy induction, information ex- traction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scal- ability. We present OntoUSP, a system that induces and populates a probabilistic on- tology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierar- chy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate On- toUSP by using it to extract a knowledge base from biomedical abstracts and an- swer questions. OntoUSP improves on the recall of USP by 47% and greatly outperforms previous state-of-the-art ap- proaches.

We present a system that learns to fol- low navigational natural language direc- tions. Where traditional models learn from linguistic annotation or word distri- butions, our approach is grounded in the world, learning by apprenticeship from routes through a map paired with English descriptions. Lacking an explicit align- ment between the text and the reference path makes it difficult to determine what portions of the language describe which aspects of the route. We learn this corre- spondence with a reinforcement learning algorithm, using the deviation of the route we follow from the intended path as a re- ward signal. We demonstrate that our sys- tem successfully grounds the meaning of spatial terms like above and south into ge- ometric properties of paths.