BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text

Irregular text refers to text with incomplete sequence information of the sentence or text that does not meet normal grammatical specifications, such as Weibo text. Existing named entity recognition algorithms recognize such text, the effect is poor due to the lack of context information. Because the attention mechanism has advantages in obtaining contextual information, we merge part-of-speech attention with the BI-LSTM-CRF model and propose a BLAC model. We tested on several public datasets and compared the results with the basic model BI-LSTM-CRF. The results show that the method we proposed has a certain improvement in the entity recognition of irregular short text.