  The Biocreative VII Track-2 challenge consists of named entity recognition,
entity-linking (or entity-normalization), and topic indexing tasks -- with
entities and topics limited to chemicals for this challenge. Named entity
recognition is a well-established problem and we achieve our best performance
with BERT-based BioMegatron models. We extend our BERT-based approach to the
entity linking task. After the second stage of pretraining BioBERT with a
metric-learning loss strategy called self-alignment pretraining (SAP), we link
entities based on the cosine similarity between their SAP-BioBERT word
embeddings. Despite the success of our named entity recognition experiments, we
find the chemical indexing task generally more challenging.
  In addition to conventional NER methods, we attempt both named entity
recognition and entity linking with a novel text-to-text or "prompt" based
method that uses generative language models such as T5 and GPT. We achieve
encouraging results with this new approach.
