Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Document centered approach to text normalization
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
User-System Cooperation in Document Annotation Based on Information Extraction
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Mixed-initiative development of language processing systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Minimizing manual annotation cost in supervised training from corpora
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Adapting svm for data sparseness and imbalance: A case study in information extraction
Natural Language Engineering
Active learning for part-of-speech tagging: accelerating corpus annotation
LAW '07 Proceedings of the Linguistic Annotation Workshop
Corrective feedback and persistent learning for information extraction
Artificial Intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A two-phase hybrid of semi-supervised and active learning approach for sequence labeling
Intelligent Data Analysis
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The preservation of the privacy of persons mentioned in text requires the ability to automatically recognize and identify names. Named entity recognition is a mature field and most current approaches are based on supervised machine learning techniques. Such learning requires the presence of labeled examples on which to train; training examples are usually provided to the learner on the form of annotated corpora. Creating and annotating corpora is a tedious, meticulous and error prone process; obtaining good training examples is a hard task in itself. This paper describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. Experimental results show that BootMark requires a human annotator to manually annotate fewer documents in order to produce a named entity recognizer with a given performance, than would be needed if the documents forming the basis for the recognizer were randomly drawn from the same corpus. The investigation further indicates that the primary gain obtained by BootMark compared to passive learning is in terms of higher recall. Thus, it is argued, the recognizers are suitable for use in privacy preservation applications.