  We investigate the task of complex NER for the English language. The task is
non-trivial due to the semantic ambiguity of the textual structure and the
rarity of occurrence of such entities in the prevalent literature. Using
pre-trained language models such as BERT, we obtain a competitive performance
on this task. We qualitatively analyze the performance of multiple
architectures for this task. All our models are able to outperform the baseline
by a significant margin. Our best performing model beats the baseline F1-score
by over 9%.
