Papers with named entity recognizers
Recognizing Complex Entity Mentions: A Review and Future Directions (P18-3)
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| Challenge: | Named entity recognition (NER) is a task of identifying and classifying named entities (NE) within text. |
| Approach: | They review existing methods for identifying and classifying named entities within text . they identify the research gap and propose a new approach to tackle these problems . |
| Outcome: | The proposed methods address the identified identified gaps in the literature and provide recommendations for future work. |
Robustness to Capitalization Errors in Named Entity Recognition (D19-55)
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| Challenge: | Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text. |
| Approach: | They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context. |
| Outcome: | The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text. |
Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss (2021.tacl-1)
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| Challenge: | Named entity recognition is a critical subtask of many domain-specific natural language understanding tasks. |
| Approach: | They propose a novel loss to learn named entity recognizers in the presence of missing entity annotations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. |