  In this work, we formulate the NER task as a multi-answer knowledge guided QA
task (KGQA) which helps to predict entities only by assigning B, I and O tags
without associating entity types with the tags. We provide different knowledge
contexts, such as, entity types, questions, definitions and examples along with
the text and train on a combined dataset of 18 biomedical corpora. This
formulation (a) enables systems to jointly learn NER specific features from
varied NER datasets, (b) can use knowledge-text attention to identify words
having higher similarity to provided knowledge, improving performance, (c)
reduces system confusion by reducing the prediction classes to B, I, O only,
and (d) makes detection of nested entities easier. We perform extensive
experiments of this KGQA formulation on 18 biomedical NER datasets, and through
experiments we note that knowledge helps in achieving better performance. Our
problem formulation is able to achieve state-of-the-art results in 12 datasets.
