While word embeddings are widely used for a variety of tasks and substantially improve the performance, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, embeddings of rare words are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and unknown words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model, obtaining up to 6.2% absolute gain in F-score. In cross-genre experiments on six genres in OntoNotes, our model improves the performance for most genre pairs and achieves 2.3% absolute F-score gain on average1.