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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish

    Researchers have developed a novel framework called Comonadic Morphophonology to handle complex context-dependent morphological rules in Finnish. This new approach uses a Writer comonad to compose rules as coKleisli arrows, offering a more compositional and efficient method compared to traditional finite-state transducers or opaque neural models. The framework successfully reduces the complexity of rule representation and enables bidirectional morphology, achieving high accuracy on a Finnish dependency treebank. AI

    IMPACT Introduces a novel, compositional approach to morphological analysis, potentially improving NLP systems for morphologically rich languages.