Chinese Named Entity Recognition Augmented with Lexicon Memory
Table 3: Results on the OntoNotes-4 development set with different features
['[BOLD] Features \\ Data', '[BOLD] Features \\ Data', '[BOLD] P (%)  [BOLD] Ground truth', '[BOLD] R (%)  [BOLD] Ground truth', '[BOLD] F1 (%)  [BOLD] Ground truth', '[BOLD] P (%)  [BOLD] Automatically labelled', '[BOLD] R (%)  [BOLD] Automatically labelled', '[BOLD] F1 (%)  [BOLD] Automatically labelled']
We also trained a LSTM-CRF model as a traditional approach for comparison by NCRF++, an open source neural sequence labeling toolkit [yang2018ncrf]. The experimental results demonstrate that the features derived from the word segmentation and POS-tagging always benefit to all the models no matter they are labeled by human or produced by automatic toolkit.