FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition
Table 1: Results of different network combinations on the CoNLL-2003 English dataset
['[BOLD] Layers Character input', '[BOLD] Models LSTM', '[BOLD] Models LSTM', '[BOLD] Models LSTM', '[BOLD] Models CNN', '[BOLD] Models CNN', '[BOLD] Models CNN']
This paper reproduces the baseline models and tests the stack models. To eliminate the influence of random factors we ran the experiments three times. Concatenating two separate baseline models (Baseline+Baseline) almost did not change results, while the Baseline+Augment model produces better results. This demonstrates data augmentation is helpful to enhance the representations to generate better results. The entity-based data transformation paradigm has great potential to improve the performance of other tasks.