@inproceedings{gangwar-etal-2021-counts,
    title = "Counts@{IITK} at {S}em{E}val-2021 Task 8: {S}ci{BERT} Based Entity And Semantic Relation Extraction For Scientific Data",
    author = "Gangwar, Akash  and
      Jain, Sabhay  and
      Sourav, Shubham  and
      Modi, Ashutosh",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.semeval-1.175/",
    doi = "10.18653/v1/2021.semeval-1.175",
    pages = "1232--1238",
    abstract = "This paper presents the system for SemEval 2021 Task 8 (MeasEval). MeasEval is a novel span extraction, classification, and relation extraction task focused on finding quantities, attributes of these quantities, and additional information, including the related measured entities, properties, and measurement contexts. Our submitted system, which placed fifth (team rank) on the leaderboard, consisted of SciBERT with [CLS] token embedding and CRF layer on top. We were also placed first in Quantity (tied) and Unit subtasks, second in MeasuredEntity, Modifier and Qualifies subtasks, and third in Qualifier subtask."
}