Topological quantification of ambiguity in semantic search
#topological data analysis #persistent homology #sentence embeddings #semantic ambiguity #semantic search #Wasserstein distance #homology groups #polysemy #natural language processing
📌 Key Takeaways
- Researchers link semantic ambiguity to topological features of sentence‑embedding neighborhoods
- Extension of polysemy studies from word to full sentence level
- Introduced two persistent homology metrics: 1‑Wasserstein norm of H0 and max loop lifetime of H1
- Quantifies query ambiguity during semantic search
- Published on arXiv in June 2024
📖 Full Retelling
A team of researchers announced a new method for measuring semantic ambiguity in sentence embeddings, published on arXiv on June 2024. They explored how the local topology of embedding neighborhoods encodes ambiguity, extending earlier work that linked word-level polysemy to persistent homology. By introducing two quantitative metrics—the 1‑Wasserstein norm of the zeroth homology group (H0) and the maximum loop lifetime of the first homology group (H1)—they were able to quantifiably assess a query’s ambiguity during semantic search. The study aims to provide a more rigorous, mathematically grounded understanding of how sentences that can be interpreted in multiple ways appear in high‑dimensional embedding spaces, potentially improving the precision of semantic retrieval systems.
🏷️ Themes
Computational linguistics, Topological data analysis, Semantic search, Natural language processing
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Original Source
arXiv:2406.07990v2 Announce Type: replace-cross
Abstract: We studied how the local topological structure of sentence-embedding neighborhoods encodes semantic ambiguity. Extending ideas that link word-level polysemy to non-trivial persistent homology, we generalized the concept to full sentences and quantified ambiguity of a query in a semantic search process with two persistent homology metrics: the 1-Wasserstein norm of $H_{0}$ and the maximum loop lifetime of $H_{1}$. We formalized the notion
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