AWhat changed
Fitted word/context vectors and biases to logarithmic co-occurrence counts with frequency weighting, connecting global count statistics with useful vector-space regularities.
Scientific scope
Fitted word/context vectors and biases to logarithmic co-occurrence counts with frequency weighting, connecting global count statistics with useful vector-space regularities. Inherits matrix-factorization and local-window distributional semantics. The proposed constraints motivate a model, not a theorem that semantic meanings must be linear. Frequency weighting uses empirical choices; analogy-task accuracy does not establish unrestricted language understanding or corpus-bias freedom.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits matrix-factorization and local-window distributional semantics. The proposed constraints motivate a model, not a theorem that semantic meanings must be linear. Frequency weighting uses empirical choices; analogy-task accuracy does not establish unrestricted language understanding or corpus-bias freedom.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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