A Multi-Task Approach for Improving Biomedical Named Entity Recognition by Incorporating Multi-Granularity information (2021.findings-acl)
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| Challenge: | Neural named entity recognition (BioNER) methods require large amount of annotated data, while the annotating BioNER datasets are often difficult to obtain and small in scale due to the limitations of privacy, ethics and high degree of specialization. |
| Approach: | They propose a method that utilizes latent multi-granularity information in annotated bioNER datasets to alleviate the lack of training samples. |
| Outcome: | The proposed model improves over the BioBERT baseline and can get more than 3% improvement of F1score in low-resource scenarios. |
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
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