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). This thesis aims to investigate how to design argument mining systems (detection, classification, and extraction of argumentative relationships in texts) that minimize biases introduced by training data
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: you should know why a projectional editor exists, what a metamodel buys you over a text grammar, and how a language evolves without breaking its existing models. Hands-on MPS experience is explicitly
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conditions, and non-i.i.d. data—where robustness and credible inference are critical. Research topics Hybrid learning & physics-based AI — Integrating process-based models with ML Unstructured data mining
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