This paper presents our two deep learning-based approaches to participate in subtask 1 of the Chemotimelines 2024 Shared task. The first uses a fine-tuning strategy on a relatively small general domain Masked Language Model (MLM) model, with additional normalization steps obtained using a simple Large Language Model (LLM) prompting technique. The second is an LLM-based approach combining advanced automated prompt search with few-shot in-context learning using the DSPy framework.Our results confirm the continued relevance of the smaller MLM fine-tuned model. It also suggests that the automated few-shot LLM approach can perform close to the fine-tuning-based method without extra LLM normalization and be advantageous under scarce data access conditions. We finally hint at the possibility to choose between lower training examples or lower computing resources requirements when considering both methods.

Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighter than modern Large Language Models (MLMs). Recently, several MLMs have been released for the biomedical domain in French, and experiments suggest that they outperform standard French counterparts. However, no systematic evaluation comparing all models on the same corpora is available. Objective: This paper presents an 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 to standard French models CamemBERT, FlauBERT and FrAlBERT as well as multilingual mBERT using three publically available corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standard corpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact.

Extracting temporal relations usually entails identifying and classifying the relation between two mentions. However, the definition of temporal mentions strongly depends on the text type and the application domain. Clinical text in particular is complex. It may describe events that occurred at different times, contain redundant information and a variety of domain-specific temporal expressions. In this paper, we propose a novel event-independent representation of temporal relations that is task-independent and, therefore, domain-independent. We are interested in identifying homogeneous text portions from a temporal standpoint and classifying the relation between each text portion and the document creation time. Temporal relation extraction is cast as a sequence labeling task and evaluated on oncology notes. We further evaluate our temporal representation by the temporal positioning of toxicity events of chemotherapy administrated to colon and lung cancer patients described in French clinical reports. An overall macro F-measure of 0.86 is obtained for temporal relation extraction by a neural token classification model trained on clinical texts written in French. Our results suggest that the toxicity event extraction task can be performed successfully by automatically identifying toxicity events and placing them within the patient timeline (F-measure .62). The proposed system has the potential to assist clinicians in the preparation of tumor board meetings.

L’extraction de relations temporelles consiste à identifier et classifier la relation entre deux mentions. Néanmoins, la définition des mentions temporelles dépend largement du type du texte et du domained’application. En particulier, le texte clinique est complexe car il décrit des évènements se produisant à des moments différents et contient des informations redondantes et diverses expressions temporellesspécifiques au domaine. Dans cet article, nous proposons une nouvelle représentation des relations temporelles, qui est indépendante du domaine et de l’objectif de la tâche d’extraction. Nous nousintéressons à extraire la relation entre chaque portion du texte et la date de création du document. Nous formulons l’extraction de relations temporelles comme une tâche d’étiquetage de séquences.Une macro F-mesure de 0,8 est obtenue par un modèle neuronal entraîné sur des textes cliniques, écrits en français. Nous évaluons notre représentation temporelle par le positionnement temporel desévènements de toxicité des chimiothérapies.

Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, cost-benefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.