Computational Reproducibility of Named Entity Recognition methods in the biomedical domain

Ana García-Serrano, Sebastian Hennig, Andreas Nürnberger


Unsupervised Named Entity Recognition (NER) approaches do not depend on labelled data to function properly but rather on a source of knowledge, in which promising candidates can be looked up to find the corresponding concept. In the biomedical domain knowledge source like this already exists; namely the Unified Medical Language System (UMLS). In this paper, three different unsupervised NER models using UMLS, namely MetaMap, cTakes and MetaMapLite are evaluated and compared from the results published by Demner-Fushman, Rogers and Aronson (2017) and Reategui and Ratte (2018). The Unsupervised Biomedical Named Entity Recognition framework (UB-NER) is developed, with which the results of the experiments of the three models, five datasets and two NER tasks are presented.

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