Papers with CoNLL-2003 English
Evaluating the Utility of Hand-crafted Features in Sequence Labelling (D18-1)
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| Challenge: | Conventional wisdom is that hand-crafted features are redundant for deep learning models . authors propose a method for using handcrafted features in a hybrid learning approach . |
| Approach: | They propose a method for exploiting handcrafted features as part of a hybrid learning approach. |
| Outcome: | The proposed method outperforms baseline models on a named entity recognition task and reduces training requirements to 60% while maintaining the same predictive accuracy. |
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023? (2023.acl-long)
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| Challenge: | NER models trained on 20-year-old test set may not perform well on modern data. |
| Approach: | They evaluate the generalization of over 20 different models trained on the CoNLL-2003 dataset . they find no evidence of performance degradation in pre-trained Transformers . |
| Outcome: | The proposed model generalizations show that some models generalize well on new data while others do not. |
Neural Architectures for Nested NER through Linearization (P19-1)
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| Challenge: | a nested named entity recognition (NER) is a set of entities that can overlap and be labeled with more than one label. |
| Approach: | They propose two neural network architectures for nested named entity recognition . they propose to model nesting entities as multilabels and predict a sequence-to-sequence problem . |
| Outcome: | The proposed methods outperform the state-of-the-art on four corpora . the proposed models also improve on the recently published contextual embeddings . |