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.

本文通过使用不同的预训练语言模型（BERT，SciBERT 和 XLM-R）提出了三个方法来解决学术出版物中软件提及检测的子任务 I。我们的最佳系统通过一个三阶段框架来解决命名实体识别问题，实验证明我们的方法在官方数据集上取得了竞争性的性能，超过了其他参赛队伍和我们的替代方法，基于 XLM-R 模型的框架达到了 67.80% 的加权 F1 得分，在软件提及识别任务中获得了第三名。