Papers with FG-NER
An Empirical Study on Fine-Grained Named Entity Recognition (C18-1)
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Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine
| Challenge: | Named entity recognition (NER) is a well studied topic in natural language processing. |
| Approach: | They propose to remove the CNN layer and use dictionary and category embeddings to improve Japanese FG-NER performance. |
| Outcome: | The proposed method improves Japanese FG-NER F-score from 66.76% to 75.18%. |
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). |
| Approach: | They compare three transformer-based names to two non-transformer-based ones . they find transformer-derived models incrementally outperform non-tranformer models . |
| Outcome: | The proposed models outperform the studied models in most domains with respect to the F1 score. |