EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Japanese Named Entity extraction with redundant morphological analysis
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - 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
Combining outputs of multiple Japanese named entity chunkers by stacking
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A fast boosting-based learner for feature-rich tagging and chunking
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
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
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This paper proposes a method for Named Entity (NE) extraction using NE-related labels of words repeatedly collected from unlabeled data. NE-related labels of words are candidate NE classes of each word, NE classes of co-occurring words of each word, and so on. To collect NE-related labels of words, we extract NEs from unlabeled data with an NE extractor. Then we collect NE-related labels of words from the extraction results. We create a new NE extractor using the NE-related labels of each word as new features. The new NE extractor is used to collect new NE-related labels of words. The experimental results using IREX data set for Japanese NE extraction show that our method contributes improved accuracy.