Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text
Table 1: The results of two previous models, and results of this study, in which we apply a boundary assembling method. Precision, recall, and F1 scores are shown for both named entity and nominal mention. For both tasks and their overall performance, we outperform the other two models.
['Models', 'Named Entity Prec', 'Named Entity Recall', 'Named Entity F1', 'Nominal Mention Prec', 'Nominal Mention Recall', 'Nominal Mention F1', 'Overall F1']
This best model performance is achieved with a dropout rate of 0.1, and a learning rate of 0.05. Our results are compared with state-of-the-art models He and Sun Our model shows an absolute improvement of 2% for the overall F1 score.