Learning to complete sentences

Authors:
Steffen Bickel;Peter Haider;Tobias Scheffer
Affiliations:
Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany;Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany;Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

Citing 6
Cited 7

An adaptive short list for documents on the World Wide Web

Proceedings of the 2nd international conference on Intelligent user interfaces
The Reactive Keyboard

The Reactive Keyboard
Predicting UNIX Command Lines: Adjusting to User Patterns

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Text prediction for translators

Text prediction for translators
Sentence completion

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning techniques to make computers easier to use

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2

Type less, find more: fast autocompletion search with a succinct index

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Effective phrase prediction

VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Output-sensitive autocompletion search

Information Retrieval
Output-Sensitive autocompletion search

SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Time-sensitive query auto-completion

SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to personalize query auto-completion

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Behavioral dynamics on the web: Learning, modeling, and prediction

ACM Transactions on Information Systems (TOIS)

Quantified Score

Hi-index	0.00

Visualization

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.