A FIT4NER Generic Approach for Framework-Independent Medical Named Entity Recognition

This article focuses on assisting medical professionals in analyzing domain-specific texts and selecting and comparing Named Entity Recognition (NER) frameworks. It details the development and evaluation of a system that utilizes a generic approach alongside the structured Nunamaker methodology. This system empowers medical professionals to train, evaluate, and compare NER models across diverse frameworks, such as Stanford CoreNLP, spaCy, and Hugging Face Transformers, independent of their specific implementations. Additionally, it introduces a concept for modeling a general training and evaluation process. Finally, experiments using various ontologies from the CRAFT corpus are conducted to assess the effectiveness of the current prototype.