Conference Proceedings

Evaluating the utility of hand-crafted features in sequence labelling

M Wu, F Liu, T Cohn

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 | Association for Computational Linguistics | Published : 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 part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a F1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive b..

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Funding Acknowledgements

This research is partly funded by the Australian Research Council (DP180102839)

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