A natural language interface to the Unix operating system using recurrent neural networks

Author

Renwick Lowell Conrad

Date of Award

12-1995

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Major Professor

Bruce MacLennan

Committee Members

David Straight, Bradley Vander Zanden

Abstract

This paper presents an experiment that was done in natural language processing using a recurrent neural network, specifically attempting to construct a natural language interface to the Unix operating system. Most natural language experimentation with neural networks has been done using a small vocabulary. The experiments in this paper were done to try to address what would happen using a larger vocabulary. Several different trials were run using binary inputs, bipolar inputs and a variety of different network architecture. When the input to the network was binary the network was able to correctly classify 85 percent of the sentences using the training set; when using bipolar input, the net was only able to classify 62 percent of the sentences correctly. Neither network did well with generalization.

Recommended Citation

Conrad, Renwick Lowell, "A natural language interface to the Unix operating system using recurrent neural networks. " Master's Thesis, University of Tennessee, 1995.
https://trace.tennessee.edu/utk_gradthes/11081