Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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
Exploiting unannotated corpora for tagging and chunking
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Estimating class priors in domain adaptation for word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Reranking and self-training for parser adaptation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Comparisons of sequence labeling algorithms and extensions
Proceedings of the 24th international conference on Machine learning
Learning to Tag and Tagging to Learn: A Case Study on Wikipedia
IEEE Intelligent Systems
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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We discuss the problem of model adaptation for the task of named entity recognition with respect to the variation of label distributions in data from different domains. We investigate an adaptive extension of the sequence perceptron, where the adaptive component includes parameters estimated from unlabelled data in combination with background knowledge in the form of gazetteers. We apply this idea empirically on adaptation experiments involving two newswire datasets from different domains and compare with other popular methods such as self training and structural correspondence learning.