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Chinese Named Entity Recognition with Graph-based Semi-supervised Learning Model
Han, Aaron Li-Feng1; Xiaodong Zeng2; Derek F. Wong2; Lidia S. Chao2
2015
Conference Namethe Eighth SIGHAN Workshop on Chinese Language Processing
Source PublicationProceedings of the Eighth SIGHAN Workshop on Chinese Language Processing
Pages15–20
Conference DateJuly 30-31, 2015
Conference PlaceBeijing, China
Abstract

Named entity recognition (NER) plays an important role in the NLP literature. The traditional methods tend to employ large annotated corpus to achieve a high performance. Different with many semi-supervised learning models for NER task, in this paper, we employ the graph-based semi-supervised learning (GBSSL) method to utilize the freely available unlabeled data. The experiment shows that the unlabeled corpus can enhance the state-of-theart conditional random field (CRF) learning model and has potential to improve the tagging accuracy even though the margin is a little weak and not satisfying in current experiments.

URLView the original
Language英语
Fulltext Access
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Institute for Logic, Language and Computation, University of Amsterdam Science Park 107, 1098 XG Amsterdam, The Netherlands
2.NLP2CT Laboratory/Department of Computer and Information Science University of Macau, Macau S.A.R., China
Recommended Citation
GB/T 7714
Han, Aaron Li-Feng,Xiaodong Zeng,Derek F. Wong,et al. Chinese Named Entity Recognition with Graph-based Semi-supervised Learning Model[C],2015:15–20.
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