Chemical Entity Recognition
2 papers with code • 0 benchmarks • 1 datasets
Chemical Entity Recognition (CER) is a fundamental task in biomedical text mining and Natural Language Processing (NLP). It involves the identification and classification of chemical entities in textual data, such as scientific literature. These entities can encompass a broad range of concepts including chemical compounds, drugs, elements, ions or functional groups. Given the complexity and variety of chemical nomenclature, the CER task represents a significant challenge for LLMs, and their performance in this task can provide important insights into their overall capabilities in the biomedical domain.
Benchmarks
These leaderboards are used to track progress in Chemical Entity Recognition
Chem-FINESE
Most implemented papers
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields.
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges.
