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Found 15 papers, 4 papers with code
The Quaero French Medical Corpus: A Ressource for Medical Entity Recognition and Normalization
Overall, a total of 26, 409 entity annotations were mapped to 5, 797 unique UMLS concepts.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development.
Few shot clinical entity recognition in three languages: Masked language models outperform LLM prompting
Results are consistent over the three languages and suggest that few-shot learning using Large language models is not production ready for named entity recognition in the clinical domain.
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world.
A Benchmark Evaluation of Clinical Named Entity Recognition in French
Objective: This paper presentsan evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them tostandard French models CamemBERT, FlauBERT and FrALBERT as well as multilingual mBERT using three publicallyavailable corpora for clinical named entity recognition in French.
CLISTER : Un corpus pour la similarité sémantique textuelle dans des cas cliniques en français (CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives)
Le TAL repose sur la disponibilité de corpus annotés pour l’entraînement et l’évaluation de modèles.
Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools
Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications.
Use of a Citizen Science Platform for the Creation of a Language Resource to Study Bias in Language Models for French: A Case Study
We created three tasks on the LanguageARC citizen science platform to assist with the translation of an existing resource from English into French as well as the collection of complementary resources in native French.
French CrowS-Pairs: Extension à une langue autre que l’anglais d’un corpus de mesure des biais sociétaux dans les modèles de langue masqués (French CrowS-Pairs : Extending a challenge dataset for measuring social bias in masked language models to a language other than English)
Nous montrons que quatre modèles de langue favorisent les énoncés qui expriment des stéréotypes dans la plupart des catégories.
Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities.
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Evaluating bias, fairness, and social impact in monolingual language models is a difficult task.
CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives
We introduce a definition of similarity that is guided by clinical facts and apply it to the development of a new French corpus of 1, 000 sentence pairs manually annotated according to similarity scores.
Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set
In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque.
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English
We build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages while also characterizing biases that are specific to each country and language.
