reproducing "ner and pos when nothing is capitalized"
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Abstract (arXiv)
Capitalization is an important feature in many NLP tasks such as Named Entity Recognition (NER) or Part of Speech Tagging (POS). We are trying to reproduce results of paper which shows how to mitigate a significant performance drop when casing is mismatched between training and testing data. In particular we show that lowercasing 50% of the dataset provides the best performance, matching the claims of the original paper. We also show that we got slightly lower performance in almost all experiments we have tried to reproduce, suggesting that there might be some hidden factors impacting our performance. Lastly, we make all of our work available in a public github repository.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 17 Sep 2021.
Builds on
6 of 15 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 546
- 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
- 2019ner and pos when nothing is capitalizedcited by 15
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