@inproceedings{zhang-etal-2023-samsung,
    title = "{S}amsung Research {C}hina - {B}eijing at {S}em{E}val-2023 Task 2: An {AL}-{R} Model for Multilingual Complex Named Entity Recognition",
    author = "Zhang, Haojie  and
      Li, Xiao  and
      Gu, Renhua  and
      Qu, Xiaoyan  and
      Meng, Xiangfeng  and
      Hu, Shuo  and
      Liu, Song",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.15",
    doi = "10.18653/v1/2023.semeval-1.15",
    pages = "114--120",
    abstract = "This paper describes our system for SemEval-2023 Task 2 Multilingual Complex Named EntityRecognition (MultiCoNER II). Our teamSamsung Research China - Beijing proposesan AL-R (Adjustable Loss RoBERTa) model toboost the performance of recognizing short andcomplex entities with the challenges of longtaildata distribution, out of knowledge base andnoise scenarios. We first employ an adjustabledice loss optimization objective to overcomethe issue of long-tail data distribution, which isalso proved to be noise-robusted, especially incombatting the issue of fine-grained label confusing. Besides, we develop our own knowledgeenhancement tool to provide related contextsfor the short context setting and addressthe issue of out of knowledge base. Experimentshave verified the validation of our approaches.",
}

This paper describes our system for SemEval-2023 Task 2 Multilingual Complex Named EntityRecognition (MultiCoNER II). Our teamSamsung Research China - Beijing proposesan AL-R (Adjustable Loss RoBERTa) model toboost the performance of recognizing short andcomplex entities with the challenges of longtaildata distribution, out of knowledge base andnoise scenarios. We first employ an adjustabledice loss optimization objective to overcomethe issue of long-tail data distribution, which isalso proved to be noise-robusted, especially incombatting the issue of fine-grained label confusing. Besides, we develop our own knowledgeenhancement tool to provide related contextsfor the short context setting and addressthe issue of out of knowledge base. Experimentshave verified the validation of our approaches.
Zhang, Haojie Li, Xiao Gu, Renhua Qu, Xiaoyan Meng, Xiangfeng Hu, Shuo Liu, Song
Samsung Research China - Beijing at SemEval-2023 Task 2: An AL-R Model for Multilingual Complex Named Entity Recognition