@inproceedings{rouhizadeh-teodoro-2022-ds4dh,
    title = "{DS}4{DH} at {S}em{E}val-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models",
    author = "Rouhizadeh, Hossein  and
      Teodoro, Douglas",
    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/jlcl-multiple-ingestion/2022.semeval-1.212/",
    doi = "10.18653/v1/2022.semeval-1.212",
    pages = "1543--1548",
    abstract = "In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages. After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model. We evaluated our proposed approach on the multilingual NER dataset at SemEval-2022. The ensemble model provided consistent improvements against the individual models on the multilingual track, achieving a macro F1 performance of 65.2{\%}. However, our results were significantly outperformed by the top ranking systems, achieving thus a baseline performance."
}