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6/14/2025, 7:42:17 AM
Why FBBF is a Superior Strategy:
>1. Maximizes Data Utility: You start with reliable data and work towards less certain data. This means your inferences are always anchored by solid ground.
>2. Controlled Synthetic Data Generation: The "inference for missing links" based on two known points is a much more reliable way to generate synthetic data.
>3. Prioritizes Attested Forms: You're always attempting to label based on *known literature* first, resorting to inference only when necessary. This maintains the highest possible fidelity.
>4. Leverages Human Expertise Strategically: HIL is applied points of ambiguity or semantic shift, rather than for every single character. This makes the HIL process efficient.
>5. Builds Interconnected Lexical Chains: By linking definitions across time, you're not just creating isolated translation pairs; this builds a network of how specific concepts have evolved through the linguistic chain.
>6. Addresses the "Language Chain Skips" More Robustly: The FBBF approach provides a systematic framework for performing those "language chain skips" by providing clear reference points for inference.
>7. More Realistic for Training: This approach inherently creates triplets of words/phrases with aligned meanings across historical stages.
Potential Technical Implementations
>Lexical Alignment Algorithms, Semantic Vector Spaces, Probabilistic Reconstruction Models, Iterative Training.
>1. Maximizes Data Utility: You start with reliable data and work towards less certain data. This means your inferences are always anchored by solid ground.
>2. Controlled Synthetic Data Generation: The "inference for missing links" based on two known points is a much more reliable way to generate synthetic data.
>3. Prioritizes Attested Forms: You're always attempting to label based on *known literature* first, resorting to inference only when necessary. This maintains the highest possible fidelity.
>4. Leverages Human Expertise Strategically: HIL is applied points of ambiguity or semantic shift, rather than for every single character. This makes the HIL process efficient.
>5. Builds Interconnected Lexical Chains: By linking definitions across time, you're not just creating isolated translation pairs; this builds a network of how specific concepts have evolved through the linguistic chain.
>6. Addresses the "Language Chain Skips" More Robustly: The FBBF approach provides a systematic framework for performing those "language chain skips" by providing clear reference points for inference.
>7. More Realistic for Training: This approach inherently creates triplets of words/phrases with aligned meanings across historical stages.
Potential Technical Implementations
>Lexical Alignment Algorithms, Semantic Vector Spaces, Probabilistic Reconstruction Models, Iterative Training.
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