Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024

• URL: http://arxiv.org/abs/2405.01575v1
• Date: Tue, 23 Apr 2024 17:06:24 GMT
• Title: Software Mention Recognition with a Three-Stage Framework Based on BERTology Models at SOMD 2024
• Authors: Thuy Nguyen Thi, Anh Nguyen Viet, Thin Dang Van, Ngan Nguyen Luu Thuy,
• Abstract summary: This paper describes our systems for the sub-task I in the Software Mention Detection in Scholarly Publications shared-task. Our best performing system addresses the named entity recognition problem through a three-stage framework. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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