  We present an unsupervised explainable word embedding technique, called EVE,
which is built upon the structure of Wikipedia. The proposed model defines the
dimensions of a semantic vector representing a word using human-readable
labels, thereby it readily interpretable. Specifically, each vector is
constructed using the Wikipedia category graph structure together with the
Wikipedia article link structure. To test the effectiveness of the proposed
word embedding model, we consider its usefulness in three fundamental tasks: 1)
intruder detection - to evaluate its ability to identify a non-coherent vector
from a list of coherent vectors, 2) ability to cluster - to evaluate its
tendency to group related vectors together while keeping unrelated vectors in
separate clusters, and 3) sorting relevant items first - to evaluate its
ability to rank vectors (items) relevant to the query in the top order of the
result. For each task, we also propose a strategy to generate a task-specific
human-interpretable explanation from the model. These demonstrate the overall
effectiveness of the explainable embeddings generated by EVE. Finally, we
compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across
the three tasks, and report improvements over the state-of-the-art.
