Nona Naderi, Julien Knafou, Jenny Copara, Patrick Ruch, Douglas Teodoro

13
Nov 19, 2021
Frontiers in Research Metrics and Analytics
DOI :
10.3389/frma.2021.689803
Article
show_chart

The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records. To unlock the value of such corpora, named entity recognition (NER) methods are proposed. Inspired by the success of transformer based pretrained models for NER, we assess how individual and ensemble of deep masked language models perform across corpora of different health and life science domains—biology, chemistry, and medicine—available in different languages—English and French. Individual deep masked language models, pretrained on external corpora, are fined tuned on task specific domain and language corpora and ensembled using classical majority voting strategies. Experiments show statistically significant improvement of the ensemble models over an individual BERT based baseline model, with an overall best performance of 77% macro F1 score. We further perform a detailed analysis of the ensemble results and show how their effectiveness changes according to entity properties, such as length, corpus frequency, and annotation consistency. The results suggest that the ensembles of deep masked language models are an effective strategy for tackling NER across corpora from the health and life science domains.

Twitter
Please Log In to leave a comment.