@inproceedings{hossain-etal-2023-garner,
    title = "gar{NER} at {S}em{E}val-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition",
    author = "Hossain, Md Zobaer  and
      So, Averie Ho Zoen  and
      Silwal, Silviya  and
      Gonzalez Gongora, H. Andres  and
      Samin, Ahnaf Mozib  and
      Junaed, Jahedul Alam  and
      Mazumder, Aritra  and
      Saha, Sourav  and
      Tahsin Soha, Sabiha",
    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-1.114",
    doi = "10.18653/v1/2023.semeval-1.114",
    pages = "823--835",
    abstract = "This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We propose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the entities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity taggers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named entities, but detrimental when the contexts are irrelevant.",
}

This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We propose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the entities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity taggers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named entities, but detrimental when the contexts are irrelevant.
Hossain, Md Zobaer So, Averie Ho Zoen Silwal, Silviya Gonzalez Gongora, H. Andres Samin, Ahnaf Mozib Junaed, Jahedul Alam Mazumder, Aritra Saha, Sourav Tahsin Soha, Sabiha
garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition