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In this work, we study the problem of relational similarity by combining different word embeddings learned from different types of contexts. The word2vec model with linear bag-ofwords contexts can capture more topical and less functional similarity, while the dependency-based word embeddings with syntactic contexts can capture more functional and less topical similarity. We explore topical space and functional space simultaneously by considering these two word embeddings and different metrics. We evaluate our model on relational similarity framework, and report state-of-the-art performance on standard test collections.