Explicitly modeling the context for Chinese named-entity recognition
Current Chinese named-entity recognition(NER)models have achieved remarkable results on public datasets.However,some studies suggest that they rely too heavily on literal features of entity text.Moreover,the influence of context on entity recognition has yet to be fully explored.Existing models perform poorly in simple invariance tests.To address this problem,this paper proposes explicitly modeling the context independently,enabling the model to differentiate between contextual information and the literal information of entities.Additionally,an adapted data en-hancement method is introduced to train the context,surface name,and combination modules.Experimental results show that this approach significantly improves the model's performance in the invariance test without sacrificing recognition performance,reducing the failure rate by 2.3％compared with the benchmark model.
natural language processingChinese named-entity recognition(NER)independent context modelingdata augmentation
万方数据
维普
