自然语言问答中的语义关系识别

为了避免问句理解阶段过度依赖命名实体,通过语义关系理解中文自然语言问句中关键信息的逻辑关系,提出基于依赖结构的语义关系识别方法,从问句的依赖结构集中识别出对生成语义关系有价值的三类依赖结构集,将三类依赖结构集组合或转换得到语义关系。在中文标准问答数据集上的试验结果验证了本语义关系识别方法的有效性和可扩展性,本方法在命名实体识别失败时也可以理解中文自然语言问句。

Semantic relation recognition for natural language question answering

To avoid the deficiency of excessive dependence on named entity recognition during the understanding perio, logic relationships among vital information in Chinese natural language question were understood by semantic relation. An algorithm for recognizing semantic relations based on dependency structures was proposed, which first recognized three kinds of valuable dependency structures that were vital for obtaining semantic relations, and then combined or transformed these dependency structures to obtain semantic relations. The effectiveness and scalability of the proposed method were verified by extensive experiments over Chinese benchmark question answering datasets, and the experiments results showed that this method could also understand Chinese natural language questions when recognition of named entity failed.