【摘要】 针对从未标记的文本中抽取中文领域实体关系的问题,文中提出基于远程监督的领域实体属性关系抽取的混合方法,利用知识库中已有结构化的关系三元组,从自然语言文本中自动获取训练语料.针对远程监督方法标注数据存在大量噪声的问题,采用隐含狄利克雷分布主题模型抽取主题关键词,再与关系类型进行相似度计算和对关键词模式匹配进行去噪.最后提取词性特征、依存关系特征和短语句法树特征,并进行融合,训练关系抽取模型.实验表明,3种特征融合的F值较高,抽取性能较好.更多还原

【Abstract】 Aiming at the extraction of Chinese domain entity relationship from unlabeled text,a hybrid method of domain entity attribute extraction based on distant supervision is proposed. The structured relational three tuples in the knowledge base are applied to obtain the training corpus automatically from the natural language text. Due to the large amount of noise in the annotation data of distant supervision method,the latent Dirichlet allocation( LDA) topic model for topic keyword extraction is adopted,and then the similarity calculation with relationship type and keyword pattern matching for denoising are performed. Finally,the part-of-speech feature,the dependency feature and the phrase syntax tree feature are extracted,and the relationship extraction model is trained. Experiments show that the method fusing three features produces higher F value and better extraction performance.更多还原