@inproceedings{pandey-etal-2022-multilinguals-semeval,
    title = "Multilinguals at {S}em{E}val-2022 Task 11: Transformer Based Architecture for Complex {NER}",
    author = "Pandey, Amit  and
      Daw, Swayatta  and
      Pudi, Vikram",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.semeval-1.224/",
    doi = "10.18653/v1/2022.semeval-1.224",
    pages = "1623--1629",
    abstract = "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{\%}."
}