Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.02.20.529292v1?rss=1
Authors: Yang, X., Saha, S., Venkatesan, A., Tirunagari, S., Vartak, V., McEntyre, J.
Abstract:
Named entity recognition (NER) is a widely used text-mining and natural language processing (NLP) sub-task. In recent years, deep learning methods have superseded traditional dictionary, and rule-based NER approaches. A high-quality dataset is essential to take full advantage of the recent deep learning advancements. While several gold standard corpora for biomedical entities in abstracts exist, only a few are based on full-text research articles. The Europe PMC literature database routinely annotates Gene/Proteins, Diseases and Organisms entities; to transition this pipeline from a dictionary-based to a machine learning-based approach, we have developed a human-annotated full-text corpus for these entities comprising 300 full-text open access research articles. Over 72,000 mentions of biomedical concepts have been identified within approximately 114,000 sentences. This article describes the corpus and details how to access and reuse this open community resource.
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