基于实体对弱约束的远监督关系抽取

Distant supervision for relation extraction with weak constraints of entity pairs

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。

In order to alleviate the false positive problem in distant supervision for relation extraction and improve the precision and recall rate,this paper presents a distant supervision model with weak constraints of entity pairs for relation extraction.This approach first gains constraint information of entity pairs from knowledge base and plain text,which contains key words of entity pairs and entity types.Then the model can obtain features of constraint information automatically by training neural networks.Then these features are used as weak constraints during relation prediction in company with the features of sentences.In contrast experiments,the model with weak constraints of entity pairs achieves higher precision and recall rate.Results show that weak constraints of entity pairs can effectively alleviate the false positive problem and enhance relation extraction.