Self-supervised pre-training techniques, albeit relying on large amounts of text, have enabled rapid growth in learning language representations for natural language understanding. However, as radically empirical models on sentences, they are subject to the input data distribution, inevitably incorporating data bias and reporting bias, which may lead to inaccurate understanding of sentences. To address this problem, we propose to adopt a human learner's approach: when we cannot make sense of a word in a sentence, we often consult the dictionary for specific meanings; but can the same work for empirical models? In this work, we try to inform the pre-trained masked language models of word meanings for semantics-enhanced pre-training. To achieve a contrastive and holistic view of word meanings, a definition pair of two related words is presented to the masked language model such that the model can better associate a word with its crucial semantic features. Both intrinsic and extrinsic evaluations validate the proposed approach on semantics-orientated tasks, with an almost negligible increase of training data.