基于跨度解码的嵌套命名实体识别方法OACSTPCD

Span classification is a popular method for nested named entity recognition but suffers from high complexity and data imbalance due to the need to exhaust and validate each span.Moreover,since the prediction is performed for each span individually,the dependencies among the entities present in the text sequence are ignored.To address the above problems of span classification methods,a nested named entity recognition method based on span decoding is proposed in the paper.First,the text is encoded by combining lexical features,character features,word features,and contextual features to obtain rich semantic information.Then,the possible entity start positions are identified,and the possible entity spans are exhausted on this basis to reduce the potential entity spans to some extent.Finally,the type of entity span corre-sponding to each start is predicted one by one using a decoder based on an attention mechanism.The decoding process passes the predicted entity information,and thus captures and learns the dependencies between entities.Experimental results show that span decoding can effectively improve span classification,and the proposed method improves F1 scores by 0.45 and 0.14 percentage points on the public English nested entity datasets ACE2005 and GENIA,respectively.