We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dmis-lab/bern2 | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| async_tell_inputfile | Ran | dmis-lab/bern2/bern2/bern2.py code served (permissive licence) · get_code("7ed778480bbe602e") |
| sync_tell_inputfile | Ran | dmis-lab/bern2/bern2/bern2.py code served (permissive licence) · get_code("171bca6004f4681e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.02080")
get_code_for_paper("2201.02080")
have("2201.02080")
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