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

摘要

This paper describes Team UWM's system for the Task 6 of SemEval 2014 for doing supervised semantic parsing of robotic spatial commands. An existing semantic parser, Krisp, was trained using the provided training data of natural language robotic spatial commands paired with their meaning representations in the formal robot command language. The entire process required very little manual effort. Without using the additional annotations of word-aligned semantic trees, the trained parser was able to exactly parse new commands into their meaning representations with 51.18% best F-measure at 72.67% precision and 39.49% recall. Results show that the parser was particularly accurate for short sentences.