Learning conditional dependence structure among latent concepts from unstructured text can provide an interpretable graph prior for downstream models. Existing methods estimate such a concept graph by modeling GloVe-style concept embeddings as matrix-normal observations with sparse row/column precision matrices, but its Gaussian assumption is usually not satisfied for modern neural embeddings. In this paper, we extend this framework to a nonparanormal matrix-normal graphical model, allowing monotone marginal transformations while preserving the conditional-independence interpretation of the row/column precision matrix. We construct matrix-valued concept embeddings from language model representations and estimate sparse row/column precision matrices via a penalized likelihood that replaces sample covariances with rank-based correlation estimates, improving the robustness in cases with skewness and heavy tails. The resulting concept graph is defined by the support of the row precision matrix and is used as a structural prior for downstream prediction. Experiments on a salary-prediction task show that the proposed nonparanormal precision graph consistently reduces absolute-error risk and improves stability over competitors. Ablations further indicate that performance gains stem from meaningful conditional-dependence structure, rather than merely from increased model capacity.