COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Making large-scale support vector machine learning practical
Advances in kernel methods
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Gene name identification and normalization using a model organism database
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Rule writing or annotation: cost-efficient resource usage for base noun phrase chunking
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A two-stage method for active learning of statistical grammars
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Investigating the effects of selective sampling on the annotation task
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
An intrinsic stopping criterion for committee-based active learning
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Stopping criteria for active learning of named entity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Multi-criteria-based strategy to stop active learning for data annotation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Confidence-based stopping criteria for active learning for data annotation
ACM Transactions on Speech and Language Processing (TSLP)
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using variance as a stopping criterion for active learning of frame assignment
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Active learning with sampling by uncertainty and density for data annotations
IEEE Transactions on Audio, Speech, and Language Processing
Inactive learning?: difficulties employing active learning in practice
ACM SIGKDD Explorations Newsletter
Uncertainty-based active learning with instability estimation for text classification
ACM Transactions on Speech and Language Processing (TSLP)
Reverse active learning for optimising information extraction training production
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A new probabilistic active sample selection algorithm for class imbalance problem
International Journal of Knowledge Engineering and Soft Data Paradigms
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Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning.