FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection

Natural language processing (NLP) applications such as named entity recognition (NER) for low-resource corpora do not benefit from recent advances in the development of large language models (LLMs) where there is still a need for larger annotated datasets. This research article introduces a methodology for generating translated versions of annotated datasets through crosslingual annotation projection. Leveraging a language agnostic BERT-based approach, it is an efficient solution to increase low-resource corpora with few human efforts and by only using already available open data resources. Quantitative and qualitative evaluations are often lacking when it comes to evaluating the quality and effectiveness of semi-automatic data generation strategies. The evaluation of our crosslingual annotation projection approach showed both effectiveness and high accuracy in the resulting dataset. As a practical application of this methodology, we present the creation of French Annotated Resource with Semantic Information for Medical Entities Detection (FRASIMED), an annotated corpus comprising 2'051 synthetic clinical cases in French. The corpus is now available for researchers and practitioners to develop and refine French natural language processing (NLP) applications in the clinical field (https://zenodo.org/record/8355629), making it the largest open annotated corpus with linked medical concepts in French.

InterpretTime: a new approach for the systematic evaluation of neural-network interpretability in time series classification

We present a novel approach to evaluate the performance of interpretability methods for time series classification, and propose a new strategy to assess the similarity between domain experts and machine data interpretation. The novel approach leverages a new family of synthetic datasets and introduces new interpretability evaluation metrics. The approach addresses several common issues encountered in the literature, and clearly depicts how well an interpretability method is capturing neural network's data usage, providing a systematic interpretability evaluation framework. The new methodology highlights the superiority of Shapley Value Sampling and Integrated Gradients for interpretability in time-series classification tasks.