Semantic Centroids and Hierarchical Density-Based Clustering for Cross-Document Software Coreference Resolution
This paper describes the system submitted to the SOMD 2026 Shared Task for Cross-Document Coreference Resolution (CDCR) of software mentions. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora. We propose a hybrid framework that combines dense semantic embeddings from a pre-trained Sentence-BERT model, Knowledge Base (KB) lookup strategy built from training-set cluster centroids using FAISS for efficient retrieval, and HDBSCAN density-based clustering for mentions that cannot be confidently assigned to existing clusters. Surface-form normalization and abbreviation resolution are applied to improve canonical name matc...
| Authors | Julia Matela, Frank Krüger |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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