An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Named entity recognition: a maximum entropy approach using global information
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
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Entity extraction without language-specific resources
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Use of support vector machines in extended named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
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 using hundreds of thousands of features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
Named entity recognition for Vietnamese
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Clinical entity recognition using structural support vector machines with rich features
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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Identifying proper names, like gene names, DNAs, or proteins is useful to help researchers to mining the text information. Learning to extract proper names in natural language text is a named entity recognition (NER) task. Previous studies focus on combining abundant human made rules, trigger words, to enhance the system performance. However these methods require domain experts to build up these rules and word set which relies on lots of human efforts. In this paper, we present a robust named entity recognition system based on support vector machines (SVM). By integrating with rich feature set and the proposed mask method, the system performance is satisfactory on the MUC-7 and biology named entity recognition tasks which outperforms famous machine learning-based method, such as hidden markov model (HMM), and maximum entropy model (MEM). We compare our method to previous systems that were performed on the same data set. The experiments show that when training with the MUC-7 data set, our system achieves 86.4 in F(β=1) rate and 81.57 for the biology corpus. Besides, our named entity system is able to handle real time processing applications, the turn around time on a 63 K words document set is less than 30 seconds.