Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets

ABSTRACT

Biomedical named entity recognition is becoming increasingly important to biomedical research due to a proliferation of articles and also due to the current pandemic disease. This paper addresses the task of automatically finding and recognizing biomedical entity types related to COVID (e.g., virus, cell, therapeutic) with tolerance rough sets. The task includes i) extracting nouns and their co-occurring contextual patterns from a large BioNER dataset related to COVID-19 and, ii) annotating unlabelled data with a semi-supervised learning algorithm using co-occurence statistics. 465,250 noun phrases and 6,222,196 contextual patterns were extracted from 29,500 articles using natural language text processing methods. Three categories were successfully classified at this time virus, cell and therapeutic. Early precision@N results demonstrate that our proposed tolerant pattern learner (TPL) is able to constrain concept drift in all 3 categories during the iterative learning process. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.