Medical Named Entity Recognition
12 papers with code • 1 benchmarks • 5 datasets
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017).
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research.
A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization
State-of-the-art studies have demonstrated the superiority of joint modelling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes.
We also investigate the effects of a small amount of additional pretraining on PubMed content, and of combining FLAIR and ELMO models.
The overwhelming amount of biomedical scientific texts calls for the development of effective language models able to tackle a wide range of biomedical natural language processing (NLP) tasks.
We introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA for the Biomedical domain.