Active learning for pipeline models

Authors:
Dan Roth;Kevin Small
Affiliations:
Department of Computer Science, University of Illinois at Urbana-Champaign;Department of Computer Science, University of Illinois at Urbana-Champaign
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Year:
2008

Citing 9
Cited 2

Toward Optimal Active Learning through Sampling Estimation of Error Reduction

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification

The Journal of Machine Learning Research
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms

EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Multi-criteria-based active learning for named entity recognition

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Solving the problem of cascading errors: approximate Bayesian inference for linguistic annotation pipelines

EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Reducing labeling effort for structured prediction tasks

AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Active learning with statistical models

Journal of Artificial Intelligence Research
Margin-Based active learning for structured output spaces

ECML'06 Proceedings of the 17th European conference on Machine Learning

Interactive feature space construction using semantic information

CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Confidence-based stopping criteria for active learning for data annotation

ACM Transactions on Speech and Language Processing (TSLP)

Quantified Score

Hi-index	0.00

Visualization

Abstract

For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, commonly referred to as a pipeline model. Typically, such scenarios are also characterized by high sample complexity, motivating the study of active learning for these situations. While most active learning research examines single predictions, we extend such work to applications which utilize pipelined predictions. Specifically, we present an adaptive strategy for combining local active learning strategies into one that minimizes the annotation requirements for the overall task. Empirical results for a three-stage entity and relation extraction system demonstrate a significant reduction in supervised data requirements when using the proposed method.