A maximum entropy approach to natural language processing
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An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Transformation based learning and data-driven lexical disambiguation: syntactic and semantic ambiguity resolution
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Improving accuracy in word class tagging through the combination of machine learning systems
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Applying system combination to base noun phrase identification
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Named entity recognition as a house of cards: classifier stacking
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A robust risk minimization based named entity recognition system
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Predicting accuracy of extracting information from unstructured text collections
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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
A robust risk minimization based named entity recognition system
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
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A high-performance semi-supervised learning method for text chunking
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Exploiting domain structure for named entity recognition
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Reducing weight undertraining in structured discriminative learning
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Expert Systems with Applications: An International Journal
Efficient text chunking using linear kernel with masked method
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Combining data-driven systems for improving Named Entity Recognition
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Extracting relevant named entities for automated expense reimbursement
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CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Empirical study on the performance stability of named entity recognition model across domains
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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Entity extraction is a boring solved problem: or is it?
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Domain adaptation with latent semantic association for named entity recognition
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
One class per named entity: exploiting unlabeled text for named entity recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Helping editors choose better seed sets for entity set expansion
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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
Voted NER system using appropriate unlabeled data
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
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CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
CRF-based active learning for Chinese named entity recognition
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Improving a state-of-the-art named entity recognition system using the world wide web
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Assessing the challenge of fine-grained named entity recognition and classification
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Improving mention detection robustness to noisy input
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Domain adaptation of rule-based annotators for named-entity recognition tasks
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Semantic entity detection by integrating CRF and SVM
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Multiobjective optimization approach for named entity recognition
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Kernel-based reranking for named-entity extraction
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Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition
Research on Language and Computation
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Automatic gazetteer generation from wikipedia
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DS'06 Proceedings of the 9th international conference on Discovery Science
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Combining data-driven systems for improving named entity recognition
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Fine tuning features and post-processing rules to improve named entity recognition
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
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Data & Knowledge Engineering
A comparative study of classifier combination applied to NLP tasks
Information Fusion
A Computer-Assisted Translation and Writing System
ACM Transactions on Asian Language Information Processing (TALIP)
Aggregating semantic annotators
Proceedings of the VLDB Endowment
Identifying the Truth: Aggregation of Named Entity Extraction Results
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Information Services and Use - Mining the Digital Information Networks
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This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based learning, and hidden Markov model) are combined under different conditions. When no gazetteer or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the F-measure error by a factor of 15 to 21% on the English data.