Adtatok
Adtatok is a term used in data encoding and natural language processing to describe a set of adaptive tokenization techniques that vary token boundaries in response to context. Unlike fixed-length or fixed-granularity tokenization, adtatok aims to balance token economy with semantic fidelity by dynamically deciding when to merge or split units of text into tokens based on statistical cues, morphological structure, or learned predictions.
The etymology of adtatok is not standardized; the name is generally interpreted as a blend of data
Implementation approaches vary but common themes include context-aware segmentation, hierarchical tokenization, and feedback-driven refinement. Some systems
Limitations include added computational overhead, potential cross-corpus inconsistency, and the challenge of evaluating tokenization quality without