What are the main challenges in biomedical named entity recognition?
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Biomedical named entity recognition (NER) faces several challenges. Limited data availability is a major challenge due to the high expertise, time, and expenses required for data annotation . Another challenge is the synonym generalization problem, where existing dictionary-based approaches struggle to identify concept synonyms not listed in the given dictionary . Inconsistent predictions and low label consistency are also challenges, especially when NER models need to operate at a document level . Additionally, the absence of publicly accessible annotated datasets hampers the use of deep learning-based methods in BioNER . Finally, the complexity and time-consuming nature of Bio-NER make it challenging to process large datasets efficiently .
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| Papers (5) | Insight |
|---|---|
| The main challenges in biomedical named entity recognition include the presence of synonyms and alternate spellings of entities, difficulty in detecting entity boundaries, nested entities, and polysemy. | |
| The main challenge in biomedical named entity recognition is the extension from a sentence-level model to a document-level model, which is not always straightforward. | |
| The main challenges in biomedical named entity recognition include difficulty in segmenting sentences with named entities, changes in spelling, long entity lengths, and ambiguity in abbreviated expressions. | |
22 May 2023 | The main challenge in biomedical named entity recognition is the identification of concept synonyms that are not listed in the given dictionary, referred to as the synonym generalization problem. |
29 May 2023 | The main challenge in biomedical named entity recognition is limited data availability, which requires high expertise, time, and expenses for annotation. |
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