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The project supports the Welsh Language Technology domain with a set of NLP tools that drive innovation and advance the development of sophisticated textual analysis solutions. The WNLT project delivers four core NLP modules; a) Word Segmentation for separating text into words b) Sentence Boundary Disambiguation for finding sentence boundaries c) Part of Speech Tagger for determining the part of speech of each word d) Morphological Analyser for identifying the root form (lemma) of words....

Alkhalil Morpho Sys is a morphosyntactic parser of Arabic words. The system can process non vocalized texts as well as partially or totally vocalized ones. Our approach is based on modelling a very large set of Arabic morphological rules, and also on integrating linguistic resources, such as the root database, vocalized patterns associated with roots, and proclitic and enclitic tables. As an output of the analysis, we have a highly informative table mainly containing vocalization of the...

Using Machine Learning, AraRooter finds the three-lettered root of any Arabic lemma with around 84% accuracy.

MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30%...