Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024
<|reference_start|>Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024: This paper describes our systems for the sub-task I in the Software Mention Detection in Scholarly Publications shared-task. We propose three approaches leveraging different pre-trained language models (BERT, SciBERT, and XLM-R) to tackle this challenge. Our bestperforming system addresses the named entity recognition (NER) problem through a three-stage framework. (1) Entity Sentence Classification - classifies sentences containing potential software mentions; (2) Entity Extraction - detects mentions within classified sentences; (3) Entity Type Classification - categorizes detected mentions into specific software types. Experiments on the official dataset demonstrate that our three-stage framework achieves competitive performance, surpassing both other participating teams and our alternative approaches. As a result, our framework based on the XLM-R-based model achieves a weighted F1-score of 67.80%, delivering our team the 3rd rank in Sub-task I for the Software Mention Recognition task.<|reference_end|>
@article{thi2024software,
    title={Software Mention Recognition with a Three-Stage Framework Based on
  BERTology Models at SOMD 2024},
    author={Thuy Nguyen Thi, Anh Nguyen Viet, Thin Dang Van, Ngan Nguyen Luu Thuy},
    journal={arXiv preprint arXiv:2405.01575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01575},
    primaryClass={cs.SE cs.AI cs.CL}
}