UWM: Applying an Existing Trainable Semantic Parser to Parse Robotic Spatial Commands

Abstract

This paper describes Team UWM’s sys-tem for the Task 6 of SemEval 2014 for doing supervised semantic parsing of robotic spatial commands. An existing semantic parser, KRISP, was trained us-ing the provided training data of natural language robotic spatial commands paired with their meaning representations in the formal robot command language. The en-tire process required very little manual ef-fort. Without using the additional annota-tions of word-aligned semantic trees, the trained parser was able to exactly parse new commands into their meaning repre-sentations with 51.18 % best F-measure at 72.67 % precision and 39.49 % recall. Re-sults show that the parser was particularly accurate for short sentences. 1