Chinese Named Entity Recognition Augmented with Lexicon Memory
Table 2: Results on OntoNotes-4 development set with different model architectures.
['[BOLD] fragment \\ Character', '[BOLD] fragment \\ Character', '[BOLD] P (%)  [BOLD] Baseline', '[BOLD] R (%)  [BOLD] Baseline', '[BOLD] F1 (%)  [BOLD] Baseline', '[BOLD] P (%)  [BOLD] Transformer', '[BOLD] R (%)  [BOLD] Transformer', '[BOLD] F1 (%)  [BOLD] Transformer', '[BOLD] P (%)  [BOLD] Bi-RNN', '[BOLD] R (%)  [BOLD] Bi-RNN', '[BOLD] F1 (%)  [BOLD] Bi-RNN']
The performances of all models will decrease of approximately 4\% in F1-score if we used the results of word segmentation and POS-tagging automatically generated by THULAC toolkit instead of the ground truth. It shows that the NER performance is significantly influenced by the results of the upstream tasks through the error propagation.