Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
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
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Text categorization for a comprehensive time-dependent benchmark
Information Processing and Management: an International Journal
Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
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 through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Predicting accuracy of extracting information from unstructured text collections
Proceedings of the 14th ACM international conference on Information and knowledge management
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 through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Named entity recognition in Vietnamese using classifier voting
ACM Transactions on Asian Language Information Processing (TALIP)
Address standardization with latent semantic association
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A context pattern induction method for named entity extraction
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
Exploiting named entity taggers in a second language
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Czech named entity corpus and SVM-based recognizer
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Effective use of TimeBank for TimeML analysis
Proceedings of the 2005 international conference on Annotating, extracting and reasoning about time and events
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Recognizing named entities in tweets
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A systematic comparison of feature-rich probabilistic classifiers for NER tasks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Chinese named entity recognition based on multilevel linguistic features
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Biased representation learning for domain adaptation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Named entity recognition for tweets
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Hi-index | 0.00 |
This paper describes a robust linear classification system for Named Entity Recognition. A similar system has been applied to the CoNLL text chunking shared task with state of the art performance. By using different linguistic features, we can easily adapt this system to other token-based linguistic tagging problems. The main focus of the current paper is to investigate the impact of various local linguistic features for named entity recognition on the CoNLL-2003 (Tjong Kim Sang and De Meulder, 2003) shared task data. We show that the system performance can be enhanced significantly with some relative simple token-based features that are available for many languages. Although more sophisticated linguistic features will also be helpful, they provide much less improvement than might be expected.