NCRF++: An Open-source Neural Sequence Labeling Toolkit
Table 2: Results using different features.
['[BOLD] Features Baseline', '[BOLD] Features WLSTM+CRF', '[BOLD] P 80.44', '[BOLD] R 87.88', '[BOLD] F 89.15']
We also investigate the influence of different features on system performance. POS tag and capital indicator are two common features on NER tasks Collobert et al. ; Huang et al. The feature embeddings are concatenated with the word embeddings as the representation of the corresponding word. Results show that both human features [POS] and [Cap] can contribute the NER system, this is consistent with previous observations Collobert et al. By utilizing LSTM or CNN to encode character sequence automatically, the system can achieve better performance on NER task.