Learning to Complete Sentences
Abstract
We consider the problem of predicting how a user will continue a given initial text fragment. Intuitively, our goal is to develop a “tab-complete” function for natural language, based on a model that is learned from text data. We consider two learning mechanisms that generate predictive models from collections of application-specific document collections: we develop an N -gram based completion method and discuss the application of instance-based learning. After developing evaluation metrics for this task, we empirically compare the model-based to the instance-based method and assess the predictability of call-center emails, personal emails, and weather reports.
Cite
Text
Bickel et al. "Learning to Complete Sentences." European Conference on Machine Learning, 2005. doi:10.1007/11564096_47Markdown
[Bickel et al. "Learning to Complete Sentences." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/bickel2005ecml-learning/) doi:10.1007/11564096_47BibTeX
@inproceedings{bickel2005ecml-learning,
title = {{Learning to Complete Sentences}},
author = {Bickel, Steffen and Haider, Peter and Scheffer, Tobias},
booktitle = {European Conference on Machine Learning},
year = {2005},
pages = {497-504},
doi = {10.1007/11564096_47},
url = {https://mlanthology.org/ecmlpkdd/2005/bickel2005ecml-learning/}
}