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Publication
ICSLP 2000
Conference paper
Hierarchical feature-based translation for scalable natural language understanding
Abstract
For complex natural language understanding systems with a large number of statistically confusable but semantically different formal commands, there are many difficulties in performing an accurate translation of a user input into a formal command in a single step. This paper addresses scalability issues in natural language understanding, and describes a method for performing the translation in a hierarchical manner. The hierarchical method improves the system accuracy, reduces the computational complexity of the translation, provides additional numerical robustness during training and decoding, and permits a more efficient packaging of the components of the natural language understanding system.