用语义中心与密度聚类解决跨文档软件实体指代消解问题
Semantic Centroids and Hierarchical Density-Based Clustering for Cross-Document Software Coreference Resolution
- 结合句向量、知识库检索与密度聚类，统一处理多文档软件指代
- 在三个子任务上分别取得0.98、0.98、0.96的CoNLL F1分数
- 适合大规模软件文献分析与知识图谱构建场景
本文介绍提交至SOMD 2026共享任务中关于跨文档软件指代消解（CDCR）的系统。针对科学文献中不一致软件提及的识别与聚类难题，提出一种混合框架：利用预训练Sentence-BERT生成密集语义嵌入，基于训练集聚类中心构建知识库（KB）检索策略（使用FAISS加速），对无法置信分配到已有簇的提及采用HDBSCAN密度聚类。通过表面形式归一化与缩写解析提升标准名匹配效果。同一核心流程用于子任务1和2；为应对子任务3的大规模设置，引入基于实体类型和归一化表面形式的阻断策略。系统在子任务1、2、3上分别获得0.98、0.98、0.96的CoNLL F1得分。
原文摘要 · Abstract (English)
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 matching. The same core pipeline is applied to Subtasks 1 and 2. To address the large scale settings of Subtask 3, the pipeline was adapted by utilising a blocking strategy based on entity types and canonicalized surface forms. Our system achieved CoNLL F1 scores of 0.98, 0.98, and 0.96 on Subtasks 1, 2, and 3 respectively.
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