Name
Abstract | ||
|---|---|---|
To control a robot in a real-world robot scenario, a real-time parser is needed to create semantic representations from natural language which can be interpreted. The parser should be able to create the hierarchical tree-like representations without consulting external systems to show its learning capabilities. We propose an efficient Echo State Network-based parser for robotic commands and only relies on the training data. The system generates a single semantic tree structure in real-time which can be executed by a robot arm manipulating objects. Four of six other approaches, which in most cases generate multiple trees and select one of them as the solution, were outperformed with 64.2% tree accuracy on difficult unseen natural language (74.1% under best conditions) on the same dataset. |
Year | Venue | Field |
|---|---|---|
2016 | ESANN | Training set,Robotic arm,Computer science,Semantic role labelling,Natural language,Tree structure,Echo state network,Natural language processing,Artificial intelligence,Parsing,Robot,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Twiefel | 1 | 12 | 4.48 |
| Xavier Hinaut | 2 | 36 | 7.93 |
| Stefan Wermter | 3 | 1100 | 151.62 |