A Benchmark Evaluation of Clinical Named Entity Recognition in French

• URL: http://arxiv.org/abs/2403.19726v1
• Date: Thu, 28 Mar 2024 07:59:58 GMT
• Title: A Benchmark Evaluation of Clinical Named Entity Recognition in French
• Authors: Nesrine Bannour, Christophe Servan, Aurélie Névéol, Xavier Tannier,
• Abstract summary: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them to standard French models CamemBERT, FlauBERT and FrALBERT. Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbonprint.
• Score: 4.430193084761607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Transformer-based language models have shown strong performance on many Natural LanguageProcessing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adaptedto different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighterthan modern Large Language Models (LLMs). Recently, several MLMs have been released for the biomedicaldomain in French, and experiments suggest that they outperform standard French counterparts. However, nosystematic evaluation comparing all models on the same corpora is available. 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. The evaluation set-up relies on gold-standardcorpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperformsDrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbonfootprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for Frenchclinical entity recognition that compares model performance consistently on nested entity recognition using metricscovering performance and environmental impact.

Related papers

• P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs [84.24644520272835]
  Large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. We present a pipeline for selecting available and reasonable benchmarks from massive ones, addressing the oversight in previous work regarding the utility of these benchmarks. We introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets.
  arXiv  Detail & Related papers  (2024-11-14T01:29:36Z)
• DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain [8.246368441549967]
  We present the first-ever publicly available French biomedical language understanding benchmark called DrBenchmark. It encompasses 20 diversified tasks, including named-entity recognition, part-of-speech tagging, question-answering, semantic textual similarity, and classification. We evaluate 8 state-of-the-art pre-trained masked language models (MLMs) on general and biomedical-specific data, as well as English specifics to assess their cross-lingual capabilities.
  arXiv  Detail & Related papers  (2024-02-20T23:54:02Z)
• CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data [1.1265248232450553]
  Transfer learning with BERT-like models has allowed major advances for French, especially for named entity recognition. We introduce CamemBERT-bio, a dedicated French biomedical model derived from a new public French biomedical dataset. Through continual pre-training, CamemBERT-bio achieves an improvement of 2.54 points of F1-score on average across various biomedical named entity recognition tasks.
  arXiv  Detail & Related papers  (2023-06-27T15:23:14Z)
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark [53.663757126289795]
  This paper introduces GAOKAO-Bench, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples. With human evaluation, we obtain the converted total score of LLMs, including GPT-4, ChatGPT and ERNIE-Bot. We also use LLMs to grade the subjective questions, and find that model scores achieve a moderate level of consistency with human scores.
  arXiv  Detail & Related papers  (2023-05-21T14:39:28Z)
• Entity-Assisted Language Models for Identifying Check-worthy Sentences [23.792877053142636]
  We propose a new uniform framework for text classification and ranking. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF's 2019 & 2020 CheckThat! Labs.
  arXiv  Detail & Related papers  (2022-11-19T12:03:30Z)
• SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese [3.0938904602244355]
  We introduce the Social Media Text Classification Evaluation (SMTCE) benchmark, as a collection of datasets and models across a diverse set of SMTC tasks. We implement and analyze the effectiveness of a variety of multilingual BERT-based models and monolingual BERT-based models for tasks in the benchmark. It provides an objective assessment of multilingual and monolingual BERT-based models on the benchmark, which will benefit future studies about BERTology in the Vietnamese language.
  arXiv  Detail & Related papers  (2022-09-21T16:33:46Z)
• Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods [0.21079694661943607]
  We compare three methods for negation detection in Dutch clinical notes. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall.
  arXiv  Detail & Related papers  (2022-09-01T14:00:13Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus [73.86656026386038]
  We introduce UmlsBERT, a contextual embedding model that integrates domain knowledge during the pre-training process. By applying these two strategies, UmlsBERT can encode clinical domain knowledge into word embeddings and outperform existing domain-specific models.
  arXiv  Detail & Related papers  (2020-10-20T15:56:31Z)
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [73.37262264915739]
  We show that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks.
  arXiv  Detail & Related papers  (2020-07-31T00:04:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.