[1]JIA Zhen,HE Dake,YANG Yan,et al.Relation extraction from Chinese online encyclopedia based on weakly supervised learnin[J].CAAI Transactions on Intelligent Systems,2015,10(1):113-119.[doi:10.10.3969/j.issn.1673-4785.201311017]
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Relation extraction from Chinese online encyclopedia based on weakly supervised learnin

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 113-119 Column: 学术论文—机器学习 Public date: 2015-03-25
Title:
Relation extraction from Chinese online encyclopedia based on weakly supervised learnin
Author(s):
JIA Zhen;  HE Dake;  YANG Yan;  YANG Yufei;  YE Zhonglin
School of Information and Science Technology, Southwest Jiaotong University, Chengdu 610031, China
Keywords:
knowledge acquisition; information extraction; relation extraction; weakly supervised learning; bootstrapping; Chinese online encyclopedia; conditional random fields; naive Bayes
CLC:
TP391
DOI:
10.10.3969/j.issn.1673-4785.201311017
Abstract:
Entity relation extraction plays an important role in the fields of information retrieval, automatic question answering and ontology learning. An entity relation extraction frame based on weakly-supervised learning is proposed in the paper. First, training data are acquired automatically from natural language texts by using relation triples in structured knowledge base. To solve the problem that the number of training data is small and features are insufficient, a bootstrapping method is used to train sentence classifiers based on naive Bayes model. This method can acquire more training data from unlabelled data. The relation extractors are trained by using conditional random fields (CRF) model. The experiment results showed that the method is feasible and effective. Compared with the existing methods state-of-the-art method, the proposed method achieves high accuracy.
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