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Measuring semantic relatedness between two words is a significant problem in many areas such as natural language processing. Existing approaches to the semantic relatedness problem mainly adopt the co-occurrence principle and regard two words as highly related if they appear in the same sentence frequently. However, such solutions suffer from low coverage and low precision because i) the two highly related words may not appear close to each other in the sentences, e.g., the synonyms; and ii) the co-occurrence of words may happen by chance rather than implying the closeness in their semantics. In this paper, we explore the latent semantics (i.e., concepts) of the words to identify highly related word pairs. We propose a hierarchical association network to specify the complex relationships among the words and the concepts, and quantify each relationship with appropriate measurements. Extensive experiments are conducted on real datasets and the results show that our proposed method improves correlation precision compared with the state-of-the-art approaches.