AI summary

This paper introduces a language model using artificial neural networks (NN) and compares it with standard statistical methods. Using data collected from the Communicator Telephone Air Travel Information System, the neural network achieves comparable performance to Katz and Jelinek-Mercer smoothing techniques.

Abstract

Currently, N-gram models are the most common and widely used models for statistical language modeling. In this paper, we investigated an alternative way to build language models, i.e., using artificial neural networks to learn the language model. Our experiment result shows that the neural network can learn a language model that has performance even better than standard statistical methods.