Distributed representations of textual elements in low dimensional vector space to capture context has gained great attention recently. Current state-of-the-art word embedding techniques compute distributed representations using co-occurrences of words within a contextual window discounting the flexibility to incorporate other contextual phenomena like temporal, geographical, and topical contexts. In this paper, we present a flexible framework that has the ability to leverage temporal,

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... cal, and topical information of documents along with the textual content to produce more effective vector representations of entities or words within a document collection. The framework first captures contextual relationships between entities collected from different relevant documents and then leverages these relationships to produce inputs of a graph, or to train a neural network to produce vectors for the entities. Through a set of rigorous experiments we test the performance of our approach and results show that our proposed solution can produce more meaningful vectors than the state-of-the-art methods.