  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.
