@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}