Residual Semantic Decomposition of Word Embeddings
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Computer Science > Computation and Language
Title:Residual Semantic Decomposition of Word Embeddings
Abstract:We introduce Residual Semantic Decomposition (RSD), a neural additive decomposition of word embeddings that balances embedding reconstruction with relational structure preservation. RSD supports recursive binary decomposition: each $K=2$ fit extracts a local semantic axis, while residuals expose information not absorbed by that axis. In manually specified paired-context diagnostics over ambiguous words, RSD separates supplied context anchors above shuffled-label controls, but entropy diagnostics show that ambiguous targets are not uniformly high-entropy boundary points in static GloVe. We therefore treat residual neighborhoods as qualitative diagnostics rather than benchmark sense predictions.
| Comments: | Short paper; includes appendix. Code and data are not included in the arXiv source package |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.17482 [cs.CL] |
| (or arXiv:2605.17482v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.17482
arXiv-issued DOI via DataCite (pending registration)
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