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. © 2005 Association for Computational Linguistics.
Cite
CITATION STYLE
Bickel, S., Haider, P., & Scheffer, T. (2005). Predicting sentences using N-gram language models. In HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 193–200). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220575.1220600
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