超越餘弦相似度：駕馭1500萬節點土耳其語同義詞圖中的語義漂移與反義詞侵入問題
Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph
January 19, 2026
作者: Ebubekir Tosun, Mehmet Emin Buldur, Özay Ezerceli, Mahmoud ElHussieni
cs.AI
摘要
神经嵌入模型存在一个显著的盲区：无法可靠区分同义词与反义词。这导致提高相似度阈值往往难以阻止反义词被错误归类。我们构建了一个大规模语义聚类系统，专门针对这一核心问题展开攻坚。该处理流程可处理1500万个词汇单元，评估高达5.2亿组潜在语义关系，最终生成290万个高精度语义聚类簇。
本系统主要实现三大突破：首先，我们通过Gemini 2.5-Flash大语言模型增强技术，结合人工校勘的词典资源，构建了包含84.3万组概念对的标注数据集，涵盖同义、反义及上下义关系。其次，我们提出专有的三元语义关系判别器，其宏观F1值达90%，实现了超越原始嵌入相似度的强健语义消歧能力。第三，我们创新性地采用软聚类到硬聚类的渐进算法，既有效抑制语义漂移（避免出现"炎热→辛辣→疼痛→抑郁"类错误传递链），又同步解决一词多义问题。该算法通过拓扑感知的双阶段扩展-剪枝流程配合拓扑投票机制，确保每个术语都能被精确划分至唯一语义连贯的聚类簇。
最终构建的语义资源能够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和低资源语言——这类语言的现有同义词数据库往往覆盖不足。
English
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.