Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
HLT '91 Proceedings of the workshop on Speech and Natural Language
HLT '93 Proceedings of the workshop on Human Language Technology
Semi-supervised learning with graphs
Semi-supervised learning with graphs
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
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
International Journal of Business Intelligence and Data Mining
Building a Graph of Names and Contextual Patterns for Named Entity Classification
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
A simple semi-supervised algorithm for named entity recognition
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Phrase clustering for discriminative learning
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Learning multilingual named entity recognition from Wikipedia
Artificial Intelligence
A joint model to identify and align bilingual named entities
Computational Linguistics
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In this paper, we present a simple yet novel method of exploiting unlabeled text to further improve the accuracy of a high-performance state-of-the art named entity recognition (NER) system. The method utilizes the empirical property that many named entities occur in one name class only. Using only unlabeled text as the additional resource, our improved NER system achieves an F1 score of 87.13%, an improvement of 1.17% in F1 score and a 8.3% error reduction on the CoNLL 2003 English NER official test set. This accuracy places our NER system among the top 3 systems in the CoNLL 2003 English shared task.