Predicting Sentences using N-Gram Language Models

Steffen Bickel, Peter Haider, Tobias Scheffer

Abstract: We explore the benefit that users in several application areas can experience from a "tab-complete" editing assistance function. We develop an evaluation metric and adapt N-gram language models to the problem of predicting the subsequent words, given an initial text fragment. Using an instance-based method as baseline, we empirically study the predictability of call-center emails, personal emails, weather reports, and cooking recipes.