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This paper focuses on learning unknown structure in conditional random fields (CRFs), especially learning both the structure and parameters of a CRF model simultaneously. By adding the l 2 -regularization to node parameters and the group l 1 -regularization to edge parameters, this structure learning problem can be cast as a convex minimization problem. Then an adaptive gradient method is proposed to solve the minimization problem. Extensive simulation experiments are presented to show thedoi:10.19139/soic.v4i3.228 fatcat:yfoi4toz75bapkpphkegzfwmti