Statistical and machine learning approaches to named entity recognition have risen to prominence in the field of natural language processing. Certain named entities, specifically biomedical software, is a challenge to identify as a named entity. One direction is investigating the use of contextual semantic information to assist in this task as alluded to by previous researchers. We introduce an ontology-driven method that experiments with both information extraction and inherited features of ontologies (e.g., embedded semantic relationships and links to entities) to automatically identify familiar and unfamiliar software names. We evaluated this method with a set of biomedical research abstracts containing software entities. Our proposed approach could be used to further augment other named entity recognition methods.
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