Foundations of statistical natural language processing
Foundations of statistical natural language processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Sample selection for statistical grammar induction
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Training conditional random fields with multivariate evaluation measures
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stopping criteria for active learning of named entity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
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We present two techniques to reduce machine learning cost, i.e., cost of manually annotating unlabeled data, for adapting existing CRF-based named entity recognition (NER) systems to new texts or domains. We introduce the tag posterior probability as the tag confidence measure of an individual NE tag determined by the base model. Dubious tags are automatically detected as recognition errors, and regarded as targets of manual correction. Compared to entire sentence posterior probability, tag posterior probability has the advantage of minimizing system cost by focusing on those parts of the sentence that require manual correction. Using the tag confidence measure, the first technique, known as active learning, asks the editor to assign correct NE tags only to those parts that the base model could not assign tags confidently. Active learning reduces the learning cost by 66%, compared to the conventional method. As the second technique, we propose bootstrapping NER, which semi-automatically corrects dubious tags and updates its model.