Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER
This work addresses complex NER for English, which is incremental as it applies existing transformer methods to a specific task.
The authors tackled complex named entity recognition (NER) in English, achieving a competitive performance with their best model beating the baseline F1-score by over 9%.
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%.