Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Hierarchical Feature-Based Translation for Scalable Natural Language Understanding

Ganesh N. Ramaswamy, Jan Kleindienst

IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA

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.

Full Paper

Bibliographic reference.  Ramaswamy, Ganesh N. / Kleindienst, Jan (2000): "Hierarchical feature-based translation for scalable natural language understanding", In ICSLP-2000, vol.3, 506-509.