BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks

Abstract

Biomedical Named Entity Recognition (NER) is a challenging problem in biomedical information processing due to the widespread ambiguity of out of context terms and extensive lexical variations. Performance on bioNER benchmarks continues to improve due to advances like BERT, GPT, and XLNet. FLAIR (1) is an alternative embedding model which is less computationally intensive than the others mentioned. We test FLAIR and its pretrained PubMed embeddings (which we term BioFLAIR) on a variety of bio NER tasks and compare those with results from BERT-type networks. We also investigate the effects of a small amount of additional pretraining on PubMed content, and of combining FLAIR and ELMO models. We find that with the provided embeddings, FLAIR performs on-par with the BERT networks - even establishing a new state of the art on one benchmark. Additional pretraining did not provide a clear benefit, although this might change with even more pretraining being done. Stacking the FLAIR embeddings with others typically does provide a boost in the benchmark results.

References in corpus (3)

• Efficient Estimation of Word Representations in Vector Space
• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• SciBERT: A Pretrained Language Model for Scientific Text

Cited by in corpus (5)

• Pre-trained Language Models in Biomedical Domain: A Systematic Survey
• ELECTRAMed: a new pre-trained language representation model for biomedical NLP
• Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
• A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
• Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets