  Word embedding is designed to represent the semantic meaning of a word with
low dimensional vectors. The state-of-the-art methods of learning word
embeddings (word2vec and GloVe) only use the word co-occurrence information.
The learned embeddings are real number vectors, which are obscure to human. In
this paper, we propose an Image-Enhanced Skip-Gram Model to learn grounded word
embeddings by representing the word vectors in the same hyper-plane with image
vectors. Experiments show that the image vectors and word embeddings learned by
our model are highly correlated, which indicates that our model is able to
provide a vivid image-based explanation to the word embeddings.
