In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. Due to the sparsity issue of news citations and the engineering difficulty in obtaining information on authors, we focus on content similarity-based methods instead of collaborative filtering-based approaches. In this paper, we explore word embedding (i.e., implicit semantics) and grounded entities (i.e., explicit semantics) to address the variety and ambiguity issues of language. We formulate the problem as a re-ranking task and integrate different similarity measures under the learning to rank framework. We evaluate our approach on a real-world dataset. The experimental results show the efficacy of our method.
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