Multi-layer Joint Learning of Chinese Nested Named Entity Recognition Based on Self-attention Mechanism

Nested named entity recognition attracts increasingly attentions due to their pervasiveness in general domain as well as in other specific domains. This paper proposes a multi-layer joint learning model for Chinese named entities recognition based on self-attention aggregation mechanism where a series of multi-layered sequence labeling sub-models are joined to recognize named entities in a bottom-up fashion. In order to capture entity semantic information in a lower layer, hidden units in an entity are aggregated using self-attention mechanism and further fed into the higher layer. We conduct extensive experiments using various entity aggregation methods. The results on the Chinese nested entity corpus transformed from the People’s Daily show that our model performs best among other competitive methods, implying that self-attention mechanism can effectively aggregate important semantic information in an entity.