The Software Mention Detection and Coreference Resolution Shared Task 2026

Abstract

Software is referenced in research papers in many different ways: full names, abbreviations, misspellings, versioned names, or indirect references via websites and citations. This makes it hard to link mentions to a single software entity, which in turn limits large-scale analyses and knowledge graph construction. The Software Mention Detection and Coreference Resolution (SOMD) shared task 2026, organized at the Natural Scientific Language Processing (NSLP) workshop at LREC 2026, focuses on clustering software mentions that refer to the same software entity. We provide three subtasks covering gold mentions, automatically extracted mentions, and mentions sampled at scale from large-scale publications. Systems are evaluated with established coreference metrics (MUC, B³, CEAFe) and their CoNLL average. This paper describes the task setup, datasets, evaluation, baseline, and the observed patterns in participant submissions, and outlines future directions for scalable software mention coreference resolution. The shared task was concluded with total five registered participants, with total 43 submissions for all subtasks. Finally, two system papers were submitted with competitive performance against baselines.

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