Natural language and spatial reasoning

Abstract

Making systems that understand language has long been a dream of artificial intelligence. This thesis develops a model for understanding language about space and movement in realistic situations. The system understands language from two real-world domains: finding video clips that match a spatial language description such as "People walking through the kitchen and then going to the dining room" and following natural language commands such as "Go down the hall towards the fireplace in the living room." Understanding spatial language expressions is a challenging problem because linguistic expressions, themselves complex and ambiguous, must be connected to real-world objects and events. The system bridges the gap between language and the world by modeling the meaning of spatial language expressions hierarchically, first capturing the semantics of spatial prepositions, and then composing these meanings into higher level structures. Corpus-based evaluations of how well the system performs in different, realistic domains show that the system effectively and robustly understands spatial language expressions.