Fixedcontext
Fixedcontext is a term used in information theory, linguistics, and machine learning to denote an approach in which the interpretation, probability, or representation of a unit is determined by a surrounding context of fixed length. A fixedcontext implies that the amount of contextual information considered around a target item is limited to a predefined window, rather than adapting to the signal length or using the entire surrounding material. In practice, fixedcontext is commonly encountered in n-gram models, where the probability of a word depends on a fixed number of preceding words, and in neural architectures that constrain attention to a fixed-size window.
The fixedcontext assumption offers predictability in memory usage and computation, enabling efficient training and inference on
Fixedcontext-related methods are used in text prediction, streaming analytics, and lightweight language models intended for devices