@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://aclanthology.org/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{\%}.",
}
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%0 Conference Proceedings
%T Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER
%A Pandey, Amit
%A Daw, Swayatta
%A Pudi, Vikram
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F pandey-etal-2022-multilinguals-semeval
%X 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%.
%R 10.18653/v1/2022.semeval-1.224
%U https://aclanthology.org/2022.semeval-1.224
%U https://doi.org/10.18653/v1/2022.semeval-1.224
%P 1623-1629
Markdown (Informal)
[Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER](https://aclanthology.org/2022.semeval-1.224) (Pandey et al., SemEval 2022)
ACL