Chemical Named Entity Recognition

Chemical Named Entity Recognition (NER) focuses on automatically identifying and classifying chemical entities (e.g., molecules, reactions) within text, primarily scientific literature and patents. Current research emphasizes leveraging large language models (LLMs) and deep learning architectures, often incorporating multimodal learning (combining text, images, and graphs) and knowledge graphs to improve accuracy and handle complex scenarios like long documents and ambiguous entities. This task is crucial for accelerating drug discovery, materials science, and other chemical research by enabling efficient data extraction, knowledge base construction, and downstream applications such as property prediction and reaction analysis, though concerns about bias in these models are emerging.