Bottom-up named entity recognition using a two-stage machine learning method
弘孝 船山, Tomohide Shibata, Sadao Kurohashi · 2009
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.