An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
The Unicode standard version 3.0
The Unicode standard version 3.0
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Japanese morphological analyzer using word co-occurrence: JTAG
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A stochastic Japanese morphological analyzer using a forward-DP backward-A* N-best search algorithm
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Identifying, the coding system and language, of on-line documents on the Internet
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Reliable measures for aligning Japanese-English news articles and sentences
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Named entity extraction based on a maximum entropy model and transformation rules
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
CJKV Information Processing
Discovering Association Rules on Experiences from Large-Scale Blog Entries
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Automatic rule learning exploiting morphological features for named entity recognition in Turkish
Journal of Information Science
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We introduce a multi-language named-entity recognition system based on HMM. Japanese, Chinese, Korean and English versions have already been implemented. In principle, it can analyze any other language if we have training data of the target language. This system has a common analytical engine and it can handle any language simply by changing the lexical analysis rules and statistical language model. In this paper, we describe the architecture and accuracy of the named-entity system, and report preliminary experiments on automatic bilingual named-entity dictionary construction using the Japanese and English named-entity recognizer.