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

ACL ID S14-2146
Title UWM: Applying an Existing Trainable Semantic Parser to Parse Robotic Spatial Commands
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

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.