Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph

• URL: http://arxiv.org/abs/2601.13251v1
• Date: Mon, 19 Jan 2026 17:37:25 GMT
• Title: Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph
• Abstract summary: We build a large-scale semantic clustering system specifically designed to tackle this problem head on.<n>We introduce a labeled dataset of 843,000 concept pairs spanning synonymy, antonymy, and co-hyponymy.<n>Second, we propose a specialized three-way semantic relation discriminator that achieves 90% macro-F1.<n>Third, we introduce a novel soft-to-hard clustering algorithm that mitigates semantic drift while simultaneously resolving polysemy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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