As an information extraction task, structured data extraction from text can be considered as a combination of named entity recognition (NER) and relation extraction (RE) between named entities. Challenges in chemical NER include the pervasive usage of abbreviations and aliases, deviations from standard nomen-clature, and the ambiguous boundaries between which a chemical entity is defined (e.g., when multiple words describe a single species). A variety of methods have been applied for chemical NER tasks. Rule-based or dictionary-based methods, such as LeadMine and ChemicalTagger , have been used to annotate reaction procedure texts or in the text parsing pipeline for constructing synthesis datasets such as SureCHEMBL 8 , Pistachio 9 , and ZeoSyn . While these algorithms are usually computationally efficient, the scope of rules and dictionary items limits their generalizability to new datasets. Various statistical model-based NER algorithms have also been proposed, often as a sequence labeling problem where the tokens in a sentence are assigned most likely tags based on token features. A popular strategy is the use of conditional random fields in combination with expertselected features or contextualized word embeddings from neural networks (recurrent networks , or transformers ).