A dual-layer CRFs based method for Chinese nested named entity recognition

While substantial studies have been performed on named entity recognition to date, nested named entity recognition as a research issue has not been well studied, especially for Chinese. In this paper, we take Chinese nested named entity recognition as a cascaded chunking problem on a sequence of wor...

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Bibliographic Details
Published in2012 9th International Conference on Fuzzy Systems and Knowledge Discovery pp. 2546 - 2550
Main Authors Chunyuan Fu, Guohong Fu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2012
Subjects
Chinese named entity recognition
Conferences
Decoding
dual-layer CRFs
Educational institutions
entity chunk
Hidden Markov models
Labeling
nested named entities
Organizations
Tagging
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Summary:While substantial studies have been performed on named entity recognition to date, nested named entity recognition as a research issue has not been well studied, especially for Chinese. In this paper, we take Chinese nested named entity recognition as a cascaded chunking problem on a sequence of words. To approach this problem, we first make a corpus-based investigation of nested structures for Chinese entities and thus propose a dual-layer conditional random fields (CRFs) based solution. To exploit more informative clues for nested named entity recognition, we employ a hybrid chunking scheme to represent the nest structures in Chinese named entities. Moreover, we have also examined the performance of different dual-layer models. Experimental results on different data sets show that the dual-layer CRFs with a hybrid chunk scheme achieve the best performance.
ISBN:9781467300254
146730025X
DOI:10.1109/FSKD.2012.6234172