The nature of statistical learning theory
The nature of statistical learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
An effective two-stage model for exploiting non-local dependencies in named entity recognition
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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This paper proposes Japanese bottom-up named entity recognition using a two-stage machine learning method. Most work has formalized Named Entity Recognition as a sequential labeling problem, in which only local information is utilized for the label estimation, and thus a long named entity consisting of several morphemes tends to be wrongly recognized. Our proposed method regards a compound noun (chunk) as a labeling unit, and first estimates the labels of all the chunks in a phrasal unit (bunsetsu) using a machine learning method. Then, the best label assignment in the bunsetsu is determined from bottom up as the CKY parsing algorithm using a machine learning method. We conducted an experimental on CRL NE data, and achieved an F measure of 89.79, which is higher than previous work.