PENNER: Pattern-enhanced Nested Named Entity Recognition in Biomedical Literature

Abstract

Many biomedical entity mentions contain other entity mentions nested inside. Most current named entity recognition (NER) systems deal with only flat entities and ignore such nested entities, which may introduce errors to subsequent tasks such as relation extraction and knowledge base completion. Recently, fully supervised methods are proposed for nested named entity recognition. Despite their success on benchmark datasets, supervised methods rely on human annotation and lead to highly specialized systems that cannot be easily adapted to new entity types. In this study, we propose PENNER, a novel and effective pattern-enhanced nested named entity recognition method that relies on massive corpora plus only very weak supervision. We compare PENNER with a state-of-the-art BioNER system, PubTator, and observe great improvement at recognizing genes, chemicals, diseases and species. PENNER can also accurately extract new types of entities, such as biological process and treatment, that are not annotated by PubTator.