Evaluating the Utility of Hand-crafted Features in Sequence Labelling https://github.com/minghao-wu/CRF-AE
Table 2: NER Performance on the CoNLL 2003 English NER shared task test set. Bold highlights best performance. † marks models trained on both the training and development sets. ‡ indicates average performance over 5 runs. ∗ indicates statistical significance on the test set against Neural-CRF by two-sample Student’s t-test at level α=0.05.
['Model', '[ITALIC] F1']
S3SS1SSS0Px4 Baseline. Observe that Neural-CRF+AE, trained either on the training set only or with the addition of the development set, achieves substantial improvements in F-score in both settings, superior to all but one of the benchmark models, highlighting the utility of hand-crafted features incorporated with the proposed auto-encoder loss. Compared against the Neural-CRF, a very strong model in itself, our model significantly improves performance, showing the positive impact of our technique for exploiting manually-engineered features. Although Peters et al.