@inproceedings{hassan-yadav-2026-transformer,
    title = "Transformer Encoders with Heuristic-Guided Contrastive Learning for Software Coreference Resolution",
    author = "Hassan, Mahmoud  and
      Yadav, Dipendra",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-workflow/2026.nslp-1.27/",
    doi = "10.63317/4zfmki62nojg",
    pages = "270--276",
    abstract = "This paper describes our system submitted to the Software Mention Detection and Coreference Resolution (SOMD) 2026 shared task, specifically for Subtask 1 (cross-document coreference resolution over gold-standard mentions) and Subtask 2 (cross-document coreference resolution over predicted mentions). The proposed approach employs a SciBERT architecture trained with Supervised Contrastive (SupCon) loss to generate dense mention representations, which are then clustered using Hierarchical Agglomerative Clustering (HAC) with average linkage. Software-aware heuristics are integrated to exploit domain-specific signals such as software name canonicalization and developer disambiguation to adjust pairwise similarity scores before clustering. The system achieved strong performance, with a CoNLL F1 score of 92.18{\%} on coreference resolution over gold-standard mentions and 91.87{\%} on coreference resolution over predicted mentions, showing significant performance of our approach in this area for human annotated and automated systems respectively"
}