Neural embeddings have a notorious blind spot: they can't reliably tell synonyms apart from antonyms. Consequently, increasing similarity thresholds often fails to prevent opposites from being grouped together. We've built a large-scale semantic clustering system specifically designed to tackle this problem head on. Our pipeline chews through 15 million lexical items, evaluates a massive 520 million potential relationships, and ultimately generates 2.9 million high-precision semantic clusters. The system makes three primary contributions. First, we introduce a labeled dataset of 843,000 concept pairs spanning synonymy, antonymy, and co-hyponymy, constructed via Gemini 2.5-Flash LLM augmentation and verified using human-curated dictionary resources. Second, we propose a specialized three-way semantic relation discriminator that achieves 90% macro-F1, enabling robust disambiguation beyond raw embedding similarity. Third, we introduce a novel soft-to-hard clustering algorithm that mitigates semantic drift preventing erroneous transitive chains (e.g., hot -> spicy -> pain -> depression) while simultaneously resolving polysemy. Our approach employs a topology-aware two-stage expansion-pruning procedure with topological voting, ensuring that each term is assigned to exactly one semantically coherent cluster. The resulting resource enables high-precision semantic search and retrieval-augmented generation, particularly for morphologically rich and low-resource languages where existing synonym databases remain sparse.

翻译：神经嵌入存在一个显著的盲区：它们无法可靠地区分同义词与反义词。因此，单纯提高相似度阈值往往难以阻止反义词被错误归入同一类别。我们构建了一个专门针对此问题的大规模语义聚类系统。该流水线处理了1500万个词汇单元，评估了5.2亿个潜在语义关系，最终生成了290万个高精度语义聚类簇。本系统主要包含三项贡献：首先，我们构建了一个包含84.3万个概念对的标注数据集，涵盖同义、反义及共类义关系，该数据集通过Gemini 2.5-Flash大语言模型增强生成，并利用人工编纂词典资源进行了验证。其次，我们提出了一种专用的三元语义关系判别器，其宏观F1值达到90%，能够实现超越原始嵌入相似度的鲁棒消歧。最后，我们设计了一种新颖的软聚类到硬聚类算法，该算法既能抑制导致错误传递链（例如：热→辣→疼痛→抑郁）的语义漂移，又能同步解决多义性问题。我们的方法采用基于拓扑感知的两阶段扩展-剪枝流程，结合拓扑投票机制，确保每个术语被精确分配至一个语义连贯的聚类簇。最终构建的资源支持高精度语义搜索与检索增强生成，尤其适用于形态复杂且现有同义词数据库稀缺的低资源语言。