agglutinativeleaning
agglutinativeleaning is a supervised learning framework that draws inspiration from agglutinative languages, where complex words are formed by stringing together morphemes. In this approach, the algorithm treats atomic units of knowledge—such as concepts, features, or sub‑tasks—as analogous to morphemes. Each unit is represented by a vector and combined through linear or tensor operations to construct a composite representation of a higher‑level concept. This compositionality mirrors how agglutinative languages build meaning by concatenating morphemes, allowing the model to encode structural relationships explicitly.
The key components of agglutinativeleaning are an encoder that learns base units, a composition operator that
Applications have been explored in natural language processing, where word embeddings are built by aggregating character
While promising, the framework faces challenges such as choosing an appropriate composition operator, avoiding over‑parameterization when