Summary of A Benchmark Evaluation Of Clinical Named Entity Recognition in French, by Nesrine Bannour (stl) et al.
A Benchmark Evaluation of Clinical Named Entity Recognition in French
by Nesrine Bannour, Christophe Servan, Aurélie Névéol, Xavier Tannier
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an evaluation of Transformer-based masked language models (MLMs) specifically designed for the biomedical domain in French. MLMs, such as CamemBERT-bio and DrBERT, have shown strong performance on various Natural Language Processing (NLP) tasks. The study aims to compare the performance of these biomedical MLMs with standard French models (CamemBERT, FlauBERT, FrALBERT), and a multilingual mBERT model on clinical named entity recognition in French using three publicly available corpora. The evaluation setup relies on gold-standard corpora released by the developers. Results indicate that CamemBERT-bio outperforms DrBERT consistently, while FlauBERT offers competitive performance, and FrAlBERT achieves the lowest carbon footprint. This study provides a benchmark evaluation of biomedical MLMs for French clinical entity recognition, highlighting their performance and environmental impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different language models to see which one is best at recognizing medical terms in French. The models are trained on special types of text called “corpora” that have been labeled as correct or not by experts. The study looks at four language models: two specifically designed for medicine (CamemBERT-bio and DrBERT), two general ones (FlauBERT and FrALBERT), and one that can understand multiple languages (mBERT). It uses three sets of text to test the models’ abilities and finds that CamemBERT-bio is the best, followed closely by FlauBERT. This study helps us understand which language model is most useful for medical professionals who need to analyze large amounts of text. |
Keywords
» Artificial intelligence » Language model » Named entity recognition » Natural language processing » Nlp » Transformer
