In English,global features of each word in a document can effectively enhance entity recognition.Unlike English,Chinese does not have explicit delimiters,and the basic unit of learning for models is characters rather than words.Therefore,intro-ducing global features for characters increases the difficulty of model learning.To address this issue,after the model extracts con-textual representations for each character,it first obtains different contextual representations for each character within the docu-ment.Then,multiple filters are applied to these different contextual representations.Finally,a gated attention mechanism controls the prediction weight of the global features.Experimental results show that the proposed model outperforms baseline models on the Resume,Weibo,and Ontonotes 4.0 datasets.
关键词
中文命名实体识别/全局特征/过滤机制/门控注意力
Key words
Chinese named entity recognition/global features/filtering mechanism/gating attention
万方数据