Abstract
The ability to understand spatial prepositions and motion in natural language will enable a variety of new applications involving systems that can respond to verbal directions, map travel guides, display incident reports, etc., providing for enhanced information extraction, question-answering, information retrieval, and more principled text to scene rendering. Until now, however, the semantics of spatial relations and motion verbs has been highly problematic. This tutorial presents a new approach to the semantics of spatial descriptions and motion expressions based on linguistically interpreted qualitative reasoning. Our approach allows for formal inference from spatial descriptions in natural language, while leveraging annotation schemes for time, space, and motion, along with machine learning from annotated corpora. We introduce a compositional semantics for motion expressions that integrates spatial primitives drawn from qualitative calculi.