NCRF++: An Open-source Neural Sequence Labeling Toolkit
Table 1: Results on three benchmarks.
['[BOLD] Models', '[BOLD] NER', '[BOLD] chunking', '[BOLD] POS']
State-of-the-art results are also listed. In this table, “Nochar” suggests a model without character sequence information. “CLSTM” and “CCNN” represent models using LSTM and CNN to encode character sequence, respectively. Similarly, “WLSTM” and “WCNN” indicate that the model uses LSTM and CNN to represent word sequence, respectively. Character information can improve model performance significantly, while using LSTM or CNN give similar improvement. Most of state-of-the-art models utilize the framework of word LSTMCRF with character LSTM or CNN features (correspond to “CLSTM+WLSTM+CRF” and “CCNN+WLSTM+CRF” of our models) Lample et al. Yang et al. Our implementations can achieve comparable results, with better NER and chunking performances and slightly lower POS tagging accuracy. Note that we use almost the same hyperparameters across all the experiments to achieve the results, which demonstrates the robustness of our implementation. The full experimental results and analysis are published in \newciteyang2018design.