Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Minghao Wu, Fei Liu, Trevor Cohn · 2018
Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora.In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component.We evaluate on the task of named entity recognition (NER), where we show that including manual features for partof-speech, word shapes and gazetteers can improve the performance of a neural CRF model.We obtain a F 1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models.We also present an ablation study showing the importance of autoencoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60%, while retaining the same predictive accuracy.