||Semantic vector embedding techniques have proven useful in learning semantic representations of data across multiple domains. A key application enabled by such techniques is the ability to measure semantic similarity between given data samples and find data most similar to a given sample. State-of-the-art embedding approaches assume all data is available on a single site. However, in coalition edge settings, data is distributed across multiple edge locations (e.g., ad hoc distributed teams supporting a coalition operation) and cannot be aggregated due to a variety of constraints. This paper presents novel unsupervised algorithms called SEEC for learning and applying semantic vector embedding in a variety of distributed coalition settings. Specifically, this paper addresses the challenge of dynamic discovery of human resources with the right context (e.g., skills) across the coalition environment (task 10.1) using novel vector embedding techniques (task 8.3) to effectively form teams for coalition tasks.