Recently, two categories of linguistic knowledge sources, word definitions from monolingual dictionaries and linguistic relations (e.g. synonymy and antonymy), have been leveraged separately to improve the traditional co-occurrence based methods for learning word embeddings. In this paper, we investigate to leverage these two kinds of resources together. Specifically, we propose a new method for word embedding specialization, named Definition Autoencoder with Semantic Injection (DASI). In our experiments1, DASI outperforms its single-knowledge-source counterparts on two semantic similarity benchmarks, and the improvements are further justified on a downstream task of dialog state tracking. We also show that DASI is superior over simple combinations of existing methods in incorporating the two knowledge sources.