吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (3): 912-919.doi: 10.13229/j.cnki.jdxbgxb20180042
Dan⁃tong OUYANG1,2(),Jun XIAO1,2,Yu⁃xin YE2()
摘要:
为缓解远监督关系抽取中的假阳性问题并进一步提高关系抽取的准确率和召回率,提出基于实体对弱约束的远监督关系抽取模型。首先,从知识库和文本中获取实体对的约束信息,约束信息由实体对关键词和实体类型两部分组成;然后,通过训练神经网络模型自动获取不同关系所对应的实体对约束信息的特征;最后,将这些特征用作弱约束联合语句特征一起进行关系预测。在对比实验中,基于实体对弱约束的模型达到了更高的准确率和召回率,表明了实体对弱约束能有效缓解假阳性问题、加强关系抽取。
中图分类号:
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