New Research on Transfer Learning Model of Named Entity Recognition

Guoliang Guan, Min Zhu
2019 Journal of Physics, Conference Series  
This paper integrates the current Google's most powerful NLP transfer learning model BERT with the traditional state-of-the-art BiLSTM-CRF model to solve the problem of named entity recognition. A bi-directional LSTM model can consider an effectively infinite amount of context on both sides of a word and eliminates the problem of limited context that applies to any feed-forward models. Google's model applied a feedforward neural network, causing its performance to weaken. We seek to solve these
more » ... issues by proposing a more powerful neural network model named BT-BiLSTM. The new neural network model has obtained F1 scores on three Chinese datasets exceeds the previous BiLSTM-CRF model, especially on the value of recall. It shows the great value of the combination of large scale none-labelled data pre-trained language model with named entity recognition, which inspire new ideas on other future work.
doi:10.1088/1742-6596/1267/1/012017 fatcat:p46ct6xxijakfhwp73ovsfw36u