Attentional SoftLexicon for Chinese named entity recognition

Some effective word embeddings have been proposed by researchers and acted in NER (Named Entity Recognition) tasks, but these embeddings often suffer from under-utilization, resulting in poorly characterized word vectors encoded. Driven by the lack of entity information, I perform lexical enhancement techniques at the input layer of the NER model for Chinese. Roughly speaking, we introduce an attention mechanism to the Softlexicon approach, which makes the embedded entity information more complete. We conducted experiments. On all four Chinese NER datasets, the performance of the improved Softlexicon is improved compared to the original one. In addition to using different datasets, we also embedded the improved Softlexicon in combination with different pre-trained models, and the attention mechanism still led to good performance.