@inproceedings{gan-etal-2022-qtrade,
title = "Qtrade {AI} at {S}em{E}val-2022 Task 11: An Unified Framework for Multilingual {NER} Task",
author = "Gan, Weichao and
Lin, Yuanping and
Yu, Guangbo and
Chen, Guimin and
Ye, Qian",
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-1.228",
doi = "10.18653/v1/2022.semeval-1.228",
pages = "1654--1664",
abstract = "This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system{'}s key contributions are as follows: 1) For multilingual NER tasks, we offered a unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource mixed-code NER task, one can easily enhanced his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we proposed a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, in the test phase, our system received macro-f1 scores of 77.66, 84.35, and 74 on task 12, task 13, and task 9.",
}
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<abstract>This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system’s key contributions are as follows: 1) For multilingual NER tasks, we offered a unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource mixed-code NER task, one can easily enhanced his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we proposed a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, in the test phase, our system received macro-f1 scores of 77.66, 84.35, and 74 on task 12, task 13, and task 9.</abstract>
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%0 Conference Proceedings
%T Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task
%A Gan, Weichao
%A Lin, Yuanping
%A Yu, Guangbo
%A Chen, Guimin
%A Ye, Qian
%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 gan-etal-2022-qtrade
%X This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system’s key contributions are as follows: 1) For multilingual NER tasks, we offered a unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource mixed-code NER task, one can easily enhanced his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we proposed a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, in the test phase, our system received macro-f1 scores of 77.66, 84.35, and 74 on task 12, task 13, and task 9.
%R 10.18653/v1/2022.semeval-1.228
%U https://aclanthology.org/2022.semeval-1.228
%U https://doi.org/10.18653/v1/2022.semeval-1.228
%P 1654-1664
Markdown (Informal)
[Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task](https://aclanthology.org/2022.semeval-1.228) (Gan et al., SemEval 2022)
ACL