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.