@inproceedings{martin-etal-2022-kddie,
title = "{KDDIE} at {S}em{E}val-2022 Task 11: Using {D}e{BERT}a for Named Entity Recognition",
author = "Martin, Caleb and
Yang, Huichen and
Hsu, William",
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-1.210",
doi = "10.18653/v1/2022.semeval-1.210",
pages = "1531--1535",
abstract = "In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.",
}
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<abstract>In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.</abstract>
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%0 Conference Proceedings
%T KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition
%A Martin, Caleb
%A Yang, Huichen
%A Hsu, William
%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 martin-etal-2022-kddie
%X In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.
%R 10.18653/v1/2022.semeval-1.210
%U https://aclanthology.org/2022.semeval-1.210
%U https://doi.org/10.18653/v1/2022.semeval-1.210
%P 1531-1535
Markdown (Informal)
[KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition](https://aclanthology.org/2022.semeval-1.210) (Martin et al., SemEval 2022)
ACL