Extracting Software Mentions and Relations using Transformers and LLM-Generated Synthetic Data at SOMD 2025

Software is an essential building block of scientific activity, but it often does not receive official citation in scholarly literature.In order to enhance research accessibility and interpretability, we built a system that identifies software mentions and their properties (e.g., version numbers, URLs) as named entities, and classify relationships between them.We finetuned Deberta based models for the Named Entity Recognition (NER) task and handled Relation Extraction (RE) as a classification problem over entity pairs.Due to the small dataset size, we employed Large Language Models to create synthetic training data for augmentation.Our system achieved strong performance, with a 65% F1 score on NER (ranking 2nd in test phase) and a 47% F1 score on RE and combined 56% F1 score, showing significant performance of our approach in this area.