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Abstract: We present an algorithmic framework for learning multiple related tasks. Ourframework exploits a form of prior knowledge that relates the output spaces ofthese tasks. We present PAC learning results that analyze the conditions underwhich such learning is possible. We present results on learning a shallowparser and named-entity recognition system that exploits our framework, showingconsistent improvements over baseline methods.



Autor: Hal Daumé III

Fuente: https://arxiv.org/







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