This paper proposes a novel framework for automatic dish discovery via word embeddings on restaurant reviews. We collect a dataset of user reviews from Yelp and parse the reviews to extract dish words. Then, we utilize the processed reviews as training texts to learn the embedding vectors of words via the skip-gram model. In the paper, a nearestneighbor like score function is proposed to rank the dishes based on their learned representations. We brief some analyses on the preliminary experiments and present a web-based visualization at http://clip.csie.org/yelp/.