Dependency-Guided LSTM-CRF for Named Entity Recognition
Table 8: F1 performance of DGLSTM-CRF with predicted dependencies against the best performing BiLSTM-CRF. †: LAS is label attachment score which is the metric for dependency evaluation.
['[EMPTY]', '[BOLD] English', '[BOLD] Chinese', '[BOLD] Catalan', '[BOLD] Spanish']
We implemented the dependency parser using the AllenNLP package Gardner et al. DGLSTM-CRF even with predicted dependencies is able to consistently outperform the BiLSTM-CRF on four languages. However, the performance is still worse than the DGLSTM-CRF with gold dependencies, especially on the Catalan and Spanish. Such results suggest that it is essential to have high-quality dependency annotations available for the proposed model.