C4.5: programs for machine learning
C4.5: programs for machine learning
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
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
Learning to recognize names across languages
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Named entity chunking techniques in supervised learning for Japanese named entity recognition
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Japanese named entity extraction evaluation: analysis of results
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
CRL/NMSU: description of the CRL/NMSU systems used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Pattern-based disambiguation for natural language processing
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
Learning pattern rules for Chinese named entity extraction
Eighteenth national conference on Artificial intelligence
Extracting important sentences with support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An agent-based approach to Chinese named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Robust extraction of named entity including unfamiliar word
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Analysis and robust extraction of changing named entities
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Automatic rule learning exploiting morphological features for named entity recognition in Turkish
Journal of Information Science
Chinese organization name recognition based on multiple features
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classified into categories such as person, organization, location, and date. NE recognition plays an essential role in information extraction systems and question answering systems. It is well known that hand-crafted systems with a large set of heuristic rules are difficult to maintain, and corpus-based statistical approaches are expected to be more robust and require less human intervention. Several statistical approaches have been reported in the literature. In a recent Japanese NE workshop, a maximum entropy (ME) system outperformed decision tree systems and most hand-crafted systems. Here, we propose an alternative method based on a simple rule generator and decision tree learning. Our experiments show that its performance is comparable to the ME approach. We also found that it can be trained more efficiently with a large set of training data and that it improves readability.